Evolutionarily Stable Sets in Quantum Penny Flip Games In game theory, an Evolutionarily Stable Set (ES set) is a set of Nash Equilibrium (NE) strategies that give the same payoffs. Similar to an Evolutionarily Stable Strategy (ES strategy), an ES set is also a strict NE. This work investigates the evolutionary stability of classical and quantum strategies in the quantum penny flip games. In particular, we developed an evolutionary game theory model to conduct a series of simulations where a population of mixed classical strategies from the ES set of the game were invaded by quantum strategies. We found that when only one of the two players' mixed classical strategies were invaded, the results were different. In one case, due to the interference phenomenon of superposition, quantum strategies provided more payoff, hence successfully replaced the mixed classical strategies in the ES set. In the other case, the mixed classical strategies were able to sustain the invasion of quantum strategies and remained in the ES set. Moreover, when both players' mixed classical strategies were invaded by quantum strategies, a new quantum ES set emerged. The strategies in the quantum ES set give both players payoff 0, which is the same as the payoff of the strategies in the mixed classical ES set of this game. Game theory applies probability theory to address uncertainty in the decision making process. In the classical world, players' decisions are stored in classical channels and the information is processed based on classical mechanics. With the advent of quantum computing, it is natural to ask if quantum mechanics would change the dynamics of game playing. To put it another way, if the decisions are stored in quantum channels, communicated and processed under quantum mechanics, would a game still produce the same result?. In 1999, Meyer [10] explored quantum superposition to play a two-person zero-sum game: the quantum penny flip game. He reported that due to the interference phenomenon of superposition, the game that originally has a Nash Equilibrium (NE) strategy that gives both players equal probability to win in the classical world has become biased: when the first player is allowed to use quantum strategies, this player always outperforms the other player who uses mixed classical strategies. Around the same time, Eisert and colleagues [2] investigated quantum entanglement in playing the Prisoners' Dilemma (PD) game. They quantized the game by first entangling two qubits using a unitary operator (explained in Section 3) that was known to both players. After that, the two qubits were distributed to the two players to encode their decisions. Once returned, the two qubits were disentangled using another unitary operator. Finally, the two qubits were measured and the results were used to calculate their payoffs. By leveraging quantum entanglement, the authors reported that the dilemma of the game did not exist any more. Moreover, the mutual-defection classical NE strategy was replaced by a quantum NE strategies that gave both players higher payoffs. Since then, various new development on quantum games have been reported, such as the quantization 1 of the Battle of the Sexes game [8] , the Hawk-Dove game [13] , the Monty Hall problem [3] and evolutionary quantum games [7] .. Although with higher payoffs, are those quantum NE strategies evolutionarily stable? An NE strategy is evolutionarily stable if a population of players have adapted the strategy, natural selection alone is sufficient to prevent it from being invaded by any mutant strategies [9] (more details in Section 2). Iqbal and Toor [6] were the first to investigate this problem using the PD game. They reported that when invaded by a group of oneparameter quantum strategies, the classical mutual-defection NE strategy remained evolutionarily stable. However, when invaded by a group of twoparameter quantum strategies, they were no longer stable and were replaced by a higher-payoff quantum mutual-collaboration strategy. Moreover, the new dominating mutual-collaboration quantum strategy was evolutionarily stable against the invasion of any two-parameter quantum strategies.. The objective of this study is to investigate the evolutionary stability of classical and quantum strategies in the quantum penny flip game, which was introduced by Meyer [10] (see Section 4 for more details). In addition to deriving analytical results, we are also interested in understanding the dynamics of strategy changes during the game. Evolutionary game theory (EGT) models [9] are ideal vehicles to provide such insight. In an EGT model, there is a strategy replication rule, in addition to the game contesting rules. The extra rule specifies how fitter strategies are multiplied and how less fit strategies are culled out of a population. Games are played repeatedly for many generations for the dynamics of the strategy changes in the population to emerge. EGT models have been successfully applied to analyze strategy changes, the success/failure of a strategy and the existence of equilibrium strategies in a game [4] .. This research developed an EGT model to study the quantum penny flip game. In particular, we used the model to conduct a series of computer simulations where a population of mixed classical strategies from the Evolutionarily Stable Set (ES set) of the game were invaded by quantum strategies. The game has two players where one player has single move while the other player has two moves. If both players use classical strategies, the two-move player does not have advantage over the one-move player; the game always ends with an equilibrium state where both players receive the same payoff of 0. However, if the two-move player is allowed to use quantum strategies while the one-move player is not, this is no longer true. Our simulation results show the following interesting phenomena:. • The two-move player's mixed classical strategies are not evolutionarily stable under the invasion of pure quantum strategies, where one particular kind of quantum strategy gives a higher payoff when playing against the one-move player's mixed classical strategies. This result is identical to that reported in [10] . Moreover, once they become the dominating strategies in the population, this kind of quantum strategies are evolutionarily stable against the invasion of any classical and quantum strategies and make the two-move player win the game with certainty.. • The one-move player's mixed classical strategies are evolutionarily stable under the invasion of pure quantum strategies when playing against the two-move player's mixed classical strategies. In other words, pure quantum strategies do not provide advantage over classical strategies for the one-move player in this game.. • When both players' mixed classical strategies were invaded by quantum strategies, a new quantum ES set emerged. The strategies in the quantum ES set give both players payoff 0, which is the same as the payoff of the strategies in the mixed classical ES set of this game.. In addition to being the first to investigate evolutionary stability of classical and quantum strategies in the quantum penny flip game, this work also made the following novel contributions:. • It proposed and implemented an EGT model to conduct systematic study of Evolutionarily Stable (ES) strategies for quantum games in general and the quantum penny flip game in particular (see Section 5).. • It analyzed and interpreted the EGT model simulation results for 3 variations of the quantum penny flip games (see Section 5.1 and Section 5.2).. • It identified a quantum ES set where all strategies in the set give both players payoff 0, which is the same as the payoff of the strategies in the mixed classical ES set of this game.. This work is significant in the following ways:. • It demonstrates how evolutionary game theory models can advance our understanding of quantum game theory.. • It demonstrates that even the simple quantum penny flip game has a rich problem landscape, which could be an inspiration for the investigation of other quantum games using a similar approach.. The paper is organized as follows. Section 2 first explains NE and ES strategies in two-player symmetric and asymmetric games. After that, their extension to the Evolutionarily Stable Set (ES set) under evolutionary game theory models are described. Section 3 gives a brief introduction to quantum information processing. In Section 4, the quantum penny flip game is described and its mixed classical NE strategies that form the ES set of the game are analyzed. Section 5 presents our work investigating classical and quantum ES sets in 3 variations of the game. Finally, Section 6 concludes the paper and outlines our future work. Classical game theory is a mature science that is frequently applied to analyze conflicts that arise during decision making in economics and social sciences. With the recent development of quantum information processing, many classical games have been quantized to investigate the game dynamics under the influence of quantum mechanics. However, there has not been work applying evolutionary game theory (EGT) models to investigate quantum game theory. This work proposed and developed an EGT model to investigate the quantum penny flip games. In particular, we used the model to conduct a series of simulations where a population of mixed classical strategies from the ES set of the game were invaded by quantum strategies. The results of our investigation are very encouraging.. First, we found that when only one of the two players' mixed classical strategies were invaded, the results were different. In one case, due to the interference phenomenon of superposition, quantum strategies provided more payoff, hence successfully replaced the mixed classical strategies in the ES set. In the other case, the mixed classical strategies were able to sustain the invasion of quantum strategies and remained in the ES set. Secondly, when both players' mixed classical strategies were invaded by quantum strategies, a new quantum ES set emerged. The strategies in the quantum ES set give both players payoff 0, which is the same as the payoff of the strategies in the mixed classical ES set of this game.. With the established EGT framework, we will continue our investigation of mixed quantum ES set in the quantum penny flip game. In particular, we will increase the number of quantum strategies used by each player to identify other quantum ES sets in this game [11] . We are also interested in applying the developed methodology to study other quantum games. Categories 1 and 2 strategies are NE because neither of the two players can change his strategy alone to improve his payoff. However, this is not the case categories 3 and 4 strategies. For example, instead of using U(0,*) and H to force a tie, Q can use U( This circular competition relationship among Q and Picard's winning strategies seems to suggest that there is no equilibrium strategies to settle those winning strategies. However, under the competitive co-evolution of the EGT model, where both P and K populations are allowed to continuously evolve new strategies to play against the new strategies evolved by the other player, a new set of compromised strategies emerged. The strategies in categories 3 and 4 are able to play against the other player's winning strategies and force a tie. Together, the four categories of quantum strategies form the ES set of this version of the quantum penny flip game.. Compared to simulation 1, where Picard's mixed-two classical strategies in the ES set were not able to change the state of the penny transformed by Q's quantum strategy, hence lost the game every time, in this simulation, the mixed-two quantum strategies in the ES set allowed Picard to always force a tie. In other words, quantum strategies have benefited Picard in this version of the quantum penny flip game.

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


Gesture modeling and animation based on a probabilistic re-creation of speaker style Animated characters that move and gesticulate appropriately with spoken text are useful in a wide range of applications. Unfortunately, this class of movement is very difficult to generate, even more so when a unique, individual movement style is required. We present a system that, with a focus on arm gestures, is capable of producing full-body gesture animation for given input text in the style of a particular performer. Our process starts with video of a person whose gesturing style we wish to animate. A tool-assisted annotation process is performed on the video, from which a statistical model of the person's particular gesturing style is built. Using this model and input text tagged with theme, rheme and focus, our generation algorithm creates a gesture script. As opposed to isolated singleton gestures, our gesture script specifies a stream of continuous gestures coordinated with speech. This script is passed to an animation system, which enhances the gesture description with additional detail. It then generates either kinematic or physically simulated motion based on this description. The system is capable of generating gesture animations for novel text that are consistent with a given performer's style, as was successfully validated in an empirical user study. People are engaged by characters with interesting personalities. However, creating quality animation for generic characters that correctly coordinates appropriate gestures with spoken text is already a challenging task. Generating movement that reflects a particular personality significantly increases the challenge of the gesture animation task, yet it is a goal towards which we must strive. This work describes one approach towards that goal. We present a system that allows the gesturing pattern of specific individuals to be modeled and then generates animation for new text from this model, complete with appropriate gestures and body movement that reflect the original subject. The movement focus is on standing characters performing arm gestures, but the approach is also applicable to the entire body.. The system operates in two phases: a preprocessing phase and a fully automatic generation phase. The preprocessing stage is shown in Figure 1 . Producing animation of a particular individual begins by collecting a video corpus for that person. In our case, we used two talk show hosts as subjects, employing about ten minutes of film of each. During an analysis step, the video is hand annotated in the tool ANVIL [Kipp 2001] , and both gesture and more general animation data are extracted. A statistical model called a Gesture Profile is built based on the annotated data. In addition, an Animation Lexicon is constructed that contains data such as the normal hand orientation for each gesture that we model. These two components provide 1 which need not be in the video corpus. The gesture script is generated from the statistical model for the individual specified by the user. This gesture script is passed to the Animation Engine, which further refines the description of the motion, using data from the animation lexicon and a set of rules described below. The animation engine produces final animated output either kinematically, or using dynamic simulation, at the user's option.. Traditionally, modeling gesture production and gesture animation are handled by well separated systems. For instance, the gesture specification system might only produce a gesture name, for example, "beat," that is then rendered by the animation system by playing a preexisting clip. In this work, we take a more tightly coupled approach that raises interesting issues of data representation and flow: What data gets generated where? Some of the detailed information needed to animate a gesture is best stored as part of the gesture model, that is, on the generation side. This information can be related to the model of a particular performer, the correlations within a stream of generated gestures, or synchronization with speech, or be definitional information for a particular gesture. Modeling this data as part of the generation process allows greater control for the gesture generation system and reduces the burden of how much the animation system needs to "know" and model. It frees the animation system from needing to access the gesture model. At the same time, representing data on the generation side can be costly in terms of modeling effort as it requires sufficient video footage, annotation work and the construction of statistical or rule based models. In keeping with principles of data encapsulation, we also wish to minimize what the gesture system needs to know about the animation system. To negotiate this trade-off, our gesture generation system produces sparse data that captures the key definitional aspects of a gesture and provides good control. The details of the motion are filled out by the animation system, which also has additional information about the figure being modeled, gesture types and the controls available. Specifically, the animation system will complete timing information, deal with spatial conflicts and add in a more rich description of gesture form as it augments the sparse data it receives from the generation stage.. In comparing our system to a motion capture approach, as well as noting the greater control afforded the generation engine in our approach, it is worth examining the range of gestures our system can produce. We currently model 28 different gestures in our animation lexicon, each of which can be generated with either or both hands in any of hundreds of spatial locations and with an arbitrary number of after-strokes (Section 3). Thousands of different combinations are possible and the animation lexicon can be easily extended. Developing an appropriate motion capture database to cover this space would be a daunting, if not prohibitive, task. Another strength of our technique is that it can be used on any subjects for whom there is Table II) . They are usually depicted with two frames from the same speaker, showing the beginning and end of the stroke (Beat, Calm, FlingDown, Progressive, Wipe) . Cup, PointingAbstract and RaisedIndexfinger are illustrated with a single frame of JL and MR each, performing the same lexeme.. adequate film of them gesturing. This means that it can potentially be used on subjects that are no longer alive or who cannot otherwise be motion captured due to cost or availability.. Two talk shows hosts, JL (Jay Leno) and MR (Marcel ReichRanicki), are used as subjects in this paper. They have different gesturing styles (e.g., Figure 3 ) and also speak different languages. This illustrates an important point: our technique can be used to create gestural animation for text in languages different to that spoken by the subject. In our animation system, we employ a skeleton model containing 89 degrees of freedom, including six degrees for world space orientation and location and 21 degrees of freedom for each hand.. In our approach to gesture synthesis we bring together a number of features that have already been studied previously but not received this level of attention: -Determination of gesture phase structure. -Precise timing for multiple strokes. -Inclusion of the gesture's spatial structure (location in space, curvature in space, hand and wrist shape, arm swivel). -Developing a broad range of gestures.. -Controller-based physical simulation of motion for gesturing. To automatically generate and animate gestures in a speaker-specific style, a human speaker has first to be studied and analyzed. Using a combination of manual labour and automatic data extraction, the key factors of speaker gesture behaviour are then stored in machinereadable form for automatic gesture generation and animation. Figure 4 gives a schematic overview of the analysis process. First, a video corpus for each speaker is annotated by hand. Both speech 8 and gestures are transcribed by human coders. This annotated corpus is used for three purposes. First, all annotated gestures are stored in the GestureDB database, as templates for automatic generation. Second, key properties for each gesture lexeme, especially the relationship between gesture and speech, are modeled with statistical tables. Both the GestureDB and the statistical model are stored in speaker-specific gesture profiles. Third, for animation, speakerspecific lexeme properties are modeled in the animation lexicon (Section 4.1.3). A video was produced for this work that includes three pairs of example animations produced by our system for the two subjects. In the first example, we show for each subject a re-creation of a particular sequence of their video corpus. The gesture scripts for these sequences are created directly from the video annotation (Figure 2) , and kinematic animation is used. These sequences validate both the fidelity of the annotation process and the ability of the animation system to generate the specified movement in the gesture script. A comparison for several gestures of a recreation of MR data to the original corpus is shown in Figure 14 .. The second examples use the same audio tracks as the first, but generate new gestures based on each speaker's respective gesture profile. Since this input was also part of the training data we subtracted the statistical data for these particular samples from the profiles before generating the new gesture scripts. These sequences were dynamically simulated. Although the generated gestures generally differ from the original (or the recreation) the animations distinctly reflect the gesticulation patterns of the modeled individuals. The resulting animations present effective gesture timing, synchronized with the original audio, and gesture forms that are consistent with the modelled subjects. This offers validation for the generation model.. The final pair of examples show gesture sequences generated by each of the subject models and dynamically animated for a new passage of synthesized English text that is not contained in either video corpus. This demonstrates that our system can operate on novel text and is language-independent, since MR's gesture profile was built on German training data. The video also illustrates the role movement plays in creating the overall impression of an utterance. Even though the timing of the speech is unlike that of either subject, the resulting animations are characteristic of each speaker.. A side by side comparison of kinematic and dynamic animations reveals small differences in timing, but the overall synchronization remains intact. A comparison of the strengths of each approach to animation is included in Table X . The strengths of the kinematic approach relate to computational cost and ease of use. The strengths of the dynamic approach, meanwhile, relate to the addition of subtle movement details that are lacking in the kinematic animation. Although subtle, we feel these effects help give the character a sense of "aliveness" that is less strong in the kinematic animation. This further validates the use of physical models and shows their relevance to synchronized gestural animation, where they have not previously been used. At the same time, as better tuning and control methods are developed, we postulate that the contribution of physical models to movement quality will become even more substantial. A side by side video comparison of the two models used on the same text sequence nicely shows the style differences between the two models (JL vs. MR). Frames from this are shown in Figure 15 . Worth noting, not only does the system produce different gestures for each speaker, it also generates very different, yet still effective, timing patterns. For instance, the last pair of frames in the figure show a case where a gesture is generated for the JL model but not the MR model.. Overall, the animations show a high variation in gesture shape, good synchronization with speech and a nice overall flow of movement. High variation stems from using positional data from the GestureDB and from creating multiple strokes. The good synchronization validates our algorithms for aligning main stroke and afterstrokes, using Steedman's concept of focus as an important gesture placement indicator. Finally, the overall flow is due to our introduction of the gesture unit as an organizational higher-level entity. To generate and animate gestures from an input of tagged text is a fairly recent endeavor, pursued in an interdisciplinary arena requiring competencies from computer animation, artificial intelligence and psychology. Cassell et al. [1994] developed a rule-based system that generates audiovisual speech, intonation, facial expression, and gesture by working on the input text's information structure which is still common practice today. Another common practice is to synchronize the gesture stroke to the accented syllable of the coexpressive word, although, as we will show, it makes sense to sometimes synchronize the stroke with a different part of the sentence. Using the same agent as Cassell et al., Noma et al. [2000] built the Virtual Presenter where gestures can be added to a text manually or with keyword-triggered rules. Animated gestures are synchronized with the following word. While the number of possible gestures is very small the focus was on how to implement meaningful rules from the literature on good public speaking. The system takes into account posture and eye contact with the audience. A more complex generation system is the Behavior Expression Animation Toolkit (BEAT) . It takes plain text as input and first runs a linguistic analysis on it before generating intonation, facial animation, and gestures. Gestures are generated using hand-made rules and are selected using priority values. While our system shares the overall goal of BEAT, to create accompanying gestures for a given text, there are a number of differences. In BEAT, a gesture is basically a "black box" that is triggered by a hand-made rule. In contrast, our system triggers gestures probabilistically and plans both the gestures' internal structure (phases, timing, shape) and their macro-structure (by creating so-called gesture units [Kendon 2004] ). Moreover, we produce a wide range of 28 different gestures 2 while BEAT seems to focus on very few samples. Most importantly, our approach produces not only natural-looking animations but a character-specific gesture style that intends to capture the individual differences of human beings. Stone et al. [2004] also use a data-driven approach to re-create a specific person's gesturing style. They re-arrange pre-recorded chunks of audio and motion captured pieces of full-body movement. Possible sentences are defined by a simple grammar. The corresponding utterance is assembled from those speech phrases and gestures that match the communicative function and minimize the required amount of time warping and blending. However, the range of both possible utterances and gestures is limited to what has been pre-recorded. In contrast, in our approach we extract abstract models of behavior that are then used to create and animate gestural behaviour on totally new input. Other relevant animation systems include EMOTE [Chi et al. 2000] which presents a kinematic system for expressive variation of arm and torso movements that is based on the analysis of Laban. Hartmann et al. [2002] present a kinematic animation system that realizes a gesture language that they have developed. Our gesture representation shares many features with theirs. We extend their representation in several ways: an additional spatial dimension is modeled (swivel angle); both world space and local hand orientation constraints are supported; additional movement features like posture, trajectory, and tension changes are modeled; and a more complex representation for afterstrokes is developed. Hartmann et al. [2006] extend their system to add expressivity to their gesture synthesis by varying activation, spatial and temporal extent, fluidity and repetition. Noot and Ruttkay [2004] are also concerned with gesturing style. A style consists of a dictionary of meaning-to-gesture mappings, motion characteristics, and modality preferences. Combining style dictionaries yields mappings for new cultural groups or individuals. In contrast to our approach, their styles are handcrafted and model the behaviour of stereotypic groups instead of real individuals. In addition, the placement and frequency of gestures is fully determined by tags in the input text, and gestures are modeled at a comparatively coarse level since the paper's focus is a style description language, and it is not concerned with animation issues. Kopp et al. [2004a Kopp et al. [ , 2004b present a gesture animation system that makes use of neurophysiological research and generates iconic gestures from object descriptions and site plans when talking about spatial domains, for example, giving directions. Iconic gestures resemble some semantic feature of an object referred to in the co-occurring speech. Recent work on sign language generation by Henerfauth et al. [2007] has looked at similar issues of spatial mapping in order to relate ASL expressions to locations in space. In contrast, in our approach the domains are mostly non-spatial. Many iconics that occur in everyday conversation are either metaphoric and therefore standardized [McNeill 1992] or verge on the emblematic (e.g., gestures for actions like drinking or counting) and thus also standardized. Therefore, Kopp et al.'s approach and ours can be considered complementary. More in line with our approach is de Ruiter's [2000] Sketch Model where both gesture and speech originate in the same module called a conceptualizer. Gestures are processed in data structures with unbound variables, so-called sketches, that can be filled according to context and using a gestuary of concept-to-shape entries. A gesture planner fills the remaining parameters like body part (which hand(s)) and spatial locations and builds a final motor program for the articulators. The model is not implemented but can predict certain phenomena in gesture-speech synchronization. Our approach shares the processing of underspecified gesture structures which we call gesture frames. Neff and Fiume [2005] present a system for modelling gesturelike movements using physical simulation, but do not model a complicated range of gestures or combine them with speech. Physical simulation has been used for many years to generate character motion with two main approaches emerging: optimization techniques that use physical laws as constraints [Witkin and Kass 1988; Popovic and Witkin 1999] and simulation techniques that forward simulate Newton's laws to generate motion [Hodgins et al. 1995] . We take a simulation approach, and in particular, follow on work in handtuned control [Hodgins et al. 1995; Faloutsos et al. 2001 ] where a proportional derivative (PD) controller is used at each character Degree of Freedom (DOF) to generate the required torques to make it move. As we have an underlying kinematic motion representation, our approach is also similar to the use of physical control to track motion capture data presented by Zordan and Hodgins [2002] , and our hand model is similar to Pollard and Zordan [2005] . To our knowledge, this is the first use of forward simulation on a character of this complexity that must synchronize its movements with speech.

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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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 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 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 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 B O O 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 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 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 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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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 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 O O 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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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Open Issues on TCP for Mobile Computing Abstract Mobile Networking is facing a growing need for efficient communication mechanisms that functionally integrate wireless and wired Internet components across their varying and distinctive transmission characteristics. Traditionally, the design of the core protocols of the wired Internet did not account for wireless architectures. For example, the end-to-end services of the wired Internet were built based largely on the functionality of the Transmission Control Protocol (TCP). TCP was designed and carefully calibrated to overcome the problems of stability, heterogeneity (receiver buffers, network bandwidth and delay), fairness in bandwidth consumption of competing flows, efficiency in utilization, and congestion control that effectively avoids situations of congestive collapse. The wireless Internet raised a number of issues that call for new protocols and architectures and for re-evaluation of well-known standards. Key issues of the wireless Internet that call for attention are the error characteristics of wireless links and the specific operations of mobile computing as well as the performance metrics to evaluate efficiency.. Standard TCP was evaluated against several proposals recently in the context of wireless networks. It became clear that the protocol needs improvements mainly because of its insufficient error control. That is, TCP error control is centered on congestion losses and ignores the possibility of transient random errors or temporary "blackouts" due to hand-offs and extended burst errors that are typical in wireless networks. Although the reader might consider that bursty drops, for example, are typical also in wired networks due to congestion, the distinctive treatment of graduated window adjustments in order to avoid a congestive collapse in situations of limited bandwidth is not necessarily the optimal strategy to recover from link errors.. More precisely, it was realized that an efficient TCP for wired/wireless networks needs to be equiped with additional mechanisms that deal with:. ¯The detection of the nature (frequency, duration, etc.) of the errors that caused packets to be dropped. This knowledge can be used to determine the appropriate error-recovery strategy. For example, hand-offs, fading channels, congestion and transient random errors call for distinct recovery stra tegies.. The adjustments of the sender's level of data transmission, within the confines of fair behavior and depending on the nature of the errors detected. This enables recovery strategies that can adjust to the underlying networks' error characteristics, device constraints and performance tradeoffs.. The protocol's energy-and time-saving capabilities. This allows for efficient applications of the protocol with mobile, battery-powered devices.. The reliable and precise detection of congestion in both the forward and the reverse path (hence, not based on RTT measurements), and possibly not by means of experiencing packet drops as in standard TCP. Such additional mechanisms could bypass the known problems of asymmetric links and reduce the impact of the timeout, which is based on measurements of the Round Trip Time (RTT), on protocol performance.. Notably, some of the protocol's inefficiencies might not become apparent when the protocol is evaluated with traditional performance metrics. For example, goodput has traditionally been used to measure the rate at which data is delivered to higher layers. On the other hand, the transmission effort expended by the protocol in order to achieve its goodput is indeed a significant metric. The appropriate evaluation for today's Internet, mainly due to the increasing presence of battery-powered devices, requires additional metrics such as energy efficiency, overhead or transmission effort. Traditional results and methods that today constitute the beacon of both fairness and efficiency are not necessarily optimal for wired/wireless networks and the Internet.. In conjunction with the above considerations a design issue has also been raised: "Where is the right place to add the integrative functionality of the wired and wireless Internet?". We outline some of the key mechanisms of TCP that require modifications in order to enhance the protocol performance in wired/wireless networks. We discuss recent results from the perspective of the protocol functionality and specific mechanisms and we classify recent proposals accordingly. We organize the rest of the paper as follows: In Section 2 we review the operations of standard TCP congestion control. In Section 3 we discuss the problems of TCP in the context of wired and wireless networks. In Section 4 we outline the requirements of error detection and we present the design of a "probing" device that satisfies these requirements. We discuss techniques for error recovery and related proposals for improving TCP perf ormance from the perspective of heterogeneous wired/wireless networks in Section 5. In Section 6 we agitate TCP's strategy from the perspective of adaptive error control and the associated energy/throughput tradeoffs. In Section 7 we present concluding remarks. Probing mechanisms at the transport level were first presented in [39] in the context of an experimental protocol. Although the desired properties (i.e., self-adjusting capability, throughput/delay monitoring) of probing devices have been clearly identified in [39, 37] , the optimal design of such devices for TCP and in the context of wired/wireless networks remains an open issue. For example, a probe cycle could consist of one extensible probe/ack exchange in the form of a packet-pair, where the level of congestion could be measured in only one RTT. This is expected to have a positive impact on throughput under infrequent errors of a transient nature. However, it may have a negative impact on the protocol's self-adjusting capability under scenarios of errors with certain density since the possibility for a probe or a probe acknowledgment to be lost would be confined. Looking at the same idea from another perspective, we could design a cycle to consist of more than one structured exchanges of probes and their acks, hence enhancing further the capability of detecting error patterns of moderated density at the cost of additional RTTs. Such a design would enforce a more conservative recovery strategy. The efficiency of different probing techniques for TCP has not been investigated.. Along the same lines, the efficiency of detecting the level of congestion calls for further attention. In [39, 44] Tsaoussidis Ø Ð propose the use of the wave pattern for detecting errors at the receiver. A wave is a fixed ¿ pattern of data exchange between sender and receiver that enables the receiver to measure the perceived level of congestion based on the time required for a wave to be delivered. More precisely, in TCP-Real [44] , the receiver observes the network dynamics (congestion level) based on the ratio of the wave size to the wave delivery time. The wave delivery time is the time difference between the reception of the first and the last segment of the wave and it could be much smaller than the RTT.. The efficiency of such measurements on the congestion level has two dimensions: how to measure congestion, and where to measure it (i.e., at the sender or the receiver). In the next section we extend the discussion on the latter, since it crucial for the error recovery tactics of TCP. As for the former, whether an estimation of congestion should be based on RTT measurements, on the packet-pair approach, on the wave pattern, or on single probes is an open issue of further research and experimentation.. Finally, although wireless proxies like WTCP may be effective when the data and their acks follow the same path through the proxy, they become useless in situations where, for example, data segments follow a satellite link through the proxy and acks follow a separate terrestrial path. Furthermore, some wireless proxy schemes assume specific versions of TCP to be effective. For example, WTCP assume TCP versions which implement timestamps for their RTT estimation. Snoop [9] , on the other hand, improves the performance of TCP Reno by exploiting its dupack mechanism to identify the loss of buffered segments over wireless links.. ¿ Yet flexible: flexibility is achieved by using different wave-levels.

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


Classification of respiratory sounds by using an artificial neural network FeatureAbmacl-In lhis paper, a classification method for respiratory sounds (RSs) in patients with asrhms and in heallhy subjccls is presented.Wavelet transform is applied to a window containing 256 samples. Elements o f lhe fealure vectors are obtained from the wavelet coellicients. The best feature elements are selected by using dynamic programming. Grow and Learn (GAL) neural network is used for lhe classification.ll is observed that RSs of patients (uilh asthma) and healthy subjects are successfully classified by the GAL network Ke~i,,~rds-Respiratory Sounds, Classification of Biomedical Signals, Artificial Neural NetworkIn order to make a reliable comparison between theSounds heard over tlie chest wall are useful diagnostic tools for puliiionarv diseases. Amongst other (electronic 'stetlioscope, Cardionics Inc.) tightly on the recording site. The recording site is chosen as lower part of tlie scapulas at the hack. To avoid the friction noise, a very thin, porous, sponge like material is settled between the skin and the ES. The ES ha5 a band-pass filter having a range from 50 Hz to 2000 Hz. The RS obtained by the At first, the RS signal obtained from a real-time measurement equipment is splitted into windows of 256 discrete data; each window is considered as a vector in a 256 dimensional space. For the two classes (normal and astliina cases), two different sets are formed for training and test purposes. Each set contains 300 vectors (150 vectors from each class) of 256 dimensions. Second, the vectors in the data sets are normalized, and features for each normalized vector are extracted. After the feature vectorS are formed, they are applied to the input of the neural network for training or classification purposes. After the training. neural network decides whether RS sigiial in a window contains information about asthma. The overall block diagram of the decision-making system is shown in Fig. I Feature extraction processes are affected by the peakto-peak magnitudes, the offset of the signal in die windowed RS signal. These effects are due to physiology, sex and age of the patient, and parameters of the measurement system. In the study, prior to the feature extraction process, power of the windowed RS signals are computed so that the rms values of the wavelet coefficients are normalized, ES are digitized by a 16-hit sound card of a personal -nonnalization

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


Interaction of TCP flows as billiards The aim of this paper is to analyze the performance of a large number of long-lived TCP controlled flows sharing many routers (or links), from the knowledge of the network parameters (capacity, buffer size, topology) and of the characteristics of each TCP flow (RTT, route etc.) when taking synchronization into account. It is shown that in the small buffer case, the dynamics of such a network can be described in terms of iterate of random piecewise affine maps, or geometrically as a billiards in the Euclidean space with as many dimensions as the number of flow classes and as many reflection facets as there are routers. This class of billiards exhibits both periodic and nonperiodic asymptotic oscillations, the characteristics of which are extremely sensitive to the parameters of the network. It is also shown that for large populations and in the presence of synchronization, aggregated throughputs exhibit fluctuations that are due to the network as a whole, that follow some complex fractal patterns, and that come on top of other and more classical flow or packet level fluctuations. The consequences on TCP's fairness are exemplified on a few typical cases of small dimension.Index Terms-Bandwidth sharing, dynamical system, flow level modeling, IP traffic, product of random matrices, TCP fairness. T HE Additive Increase, Multiplicative Decrease (AIMD) model introduced in [4] describes the joint evolution of the congestion window size of long-lived (FTP or Peer to Peer type) flows controlled by TCP and sharing a single router or link, in terms of products of random matrices. The associated large population asymptotic model which concerns the case was studied in [12] .. The present paper studies the case when the TCP flows are heterogeneous, i.e., with different Round Trip Times (RTTs) or routes, and when each flow goes through a route made of several tail-drop routers (throughout the paper, we will consider routers to be the possible bottlenecks; this could be replaced by links everywhere without altering the conclusions) in series. The corresponding model, which is introduced in Section II, will be referred to as the multi-AIMD model.. Our aim is to estimate the throughput obtained by each individual flow under the competition rules imposed by TCP, and also the fluctuations of this throughput, from the sole knowledge of the route and the RTT of each flow, and the characteristics of each router and link (buffer size, link capacity etc.) in the network. This is of course related to the classical relationships that have been obtained between the packet loss probability and TCP throughput for a given session (see, e.g., [17] ); in particular, it Manuscript was shown in [4] that the single router AIMD dynamics resulted in a dependency between these quantities that was compatible with these formulas and was actually refining them in that it allowed one to assess the influence of synchronization. The first models for the several router TCP network case are those of [13] and [9] . These papers analyzed the bandwidth sharing of different TCP flows over large networks in terms of optimization problems, and triggered a large number of further studies (see, e.g., [16] , [15] ). The prediction of the throughput in the several AQM router case has also been investigated via fixed point approximation methods for mean values in [7] . The approach that is proposed in the present paper addresses the same prediction question in the tail-drop router case. The main difference with these earlier approaches lies in the fact that we use a pathwise description of the dynamics of the interaction between flows, which takes into account discrete event phenomena that are of central importance for tail drop networks, such as congestion epochs, losses, or synchronization of sources, as well as random phenomena which all have an impact on throughput fluctuations.. More precisely, the interaction is described by a set of evolution equations that generalize the random affine map description of the AIMD (one-router) model. The basic multi-AIMD model can be seen as iterates of random piecewise affine maps. From this stochastic model, we define a large population asymptotic model. This asymptotic model can be seen as iterates of deterministic piecewise affine maps. These equations are shown to admit a geometrical representation in terms of a random or deterministic billiards in the Euclidean space. The dimension of this space is the number of different flow classes (typically, there is one flow class per route and RTT). This billiards has as many reflection facets as there are routers.. This new representation of the interaction between TCP flows over networks made of several links and routers and its exploitation are the main contributions of the present paper. We establish sufficient conditions for the asymptotic periodicity of the throughput obtained by the interacting flows, as well as a conservation law that relates the intensities with which routers experience congestion. Billiards are known to possibly exhibit nonperiodic asymptotic behaviors. We give numerical evidence that this is possible for the class of billiards considered here. We also show that the characteristics of this asymptotic behavior are extremely sensitive w.r.t. network parameters. The implications on TCP's fairness and bandwidth sharing are exemplified on a few cases of small dimension. We show however that once the periodic regime is known, fairness can be approached analytically using a mix of linear algebra and cycle formulas of Palm calculus [3] .. The validation of this billiards representation is not addressed in the present paper; it is the object of a companion paper [5] , which investigates more generally the use of this approach as a simulation tool. In addition to comparison with NS2 simulation, it is shown there that the aggregated traffic generated by this representation satisfies several empirical or statistical laws that were observed on real traces. This concerns the short time scale statistical properties observed in [20] , [24] , [1] and the empirical power law describing the sensitivity w.r.t. RTTs reported in [14] . We have introduced a model allowing one to study the bandwidth sharing operated by TCP on networks composed of several tail-drop routers or links and which takes source synchronization into account. This model is based on the interplay between three (sub) models: a deterministic network level model (the billiards), a set of more or less independent stochastic models for individual flows, where the influence of the whole network is taken into account via certain averages, and a packet level model that is only used for determining the delay of reaction of sources and the associated synchronization rates.. Each level creates its own type of fluctuations on throughput. The flow level and packet fluctuations have already been studied. Flow level fluctuations of throughput aggregates have for instance been studied in [4] and [12] and for the throughput of individual flows in [8] . The representation of TCP controlled networks as billiards has allowed us to quantify the fluctuations at network level, that is the time/space location of the congestion epochs, these local storms that are induced by the AI process here and there in the network, and that are in charge of the overall control within the drop tail setting. We produced numerical evidence that both periodic and nonperiodic asymptotic behaviors are possible for the empirical mean values of throughputs and that any slight changes in the model parameters, for instance trends in the population parameters or , could result into drastic changes for the instantaneous values achieved by empirical averages.. In [20] , [24] , and [1] , it was experimentally shown that aggregated traffic of internet traces often exhibits multifractal scaling properties at short time scales. There have been many attempts to explain these properties and to link them to TCP itself (for short time scales, TCP is the only likely explanation, whereas long time scale fluctuations have been shown to be HTTP induced). Such a multifractal scaling means that aggregated traffic trajectories are extremely irregular and have fluctuations at all short time scales. The identification of network level fluctuations with complex patterns is one more step in the direction of the classification of all types of TCP induced fluctuations that contribute to the extreme irregularity of aggregated traffic. Flow level variability seems to be enough to provide a multifractal short time scale behavior [4] . However the fact that aggregated traffic could have fluctuations with arbitrarily long periods is also a possible explanation for time and flow averages to have fluctuations over several time scales. The combination of fluctuations of all three levels seems required for a complete prediction of the global statistical structure of aggregated traffic.. The properties reported in the present paper are different from the simulation based observations on the chaotic behavior of TCP reported in [23] . The main difference lies in the fact that the properties of the present paper bear on the sensitivity of aggregated traffic (the empirical mean values as defined above) w.r.t. some topology parameter (e.g., the speed of a router), whereas the observations of [23] focus on the dependence of the throughput of individual flows w.r.t. initial conditions for a given topology. There might however be a link between the sensitivity w.r.t. initial conditions and the fact that the facet and billiards could have a nonperiodic behavior for a given topology.. We intend to continue exploring this class of models and to try to enrich it with other types of traffic than the long-lived TCP sessions on which this first step is focused.

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


An Outer Bound to the Capacity Region of the Broadcast Channel Abstract-An outer bound to the capacity region of the two-receiver discrete memoryless broadcast channel is given. The outer bound is tight for all cases where the capacity region is known. When specialized to the case of no common information, this outer bound is shown to be contained in the Körner-Marton outer bound. This containment is shown to be strict for the binary skewsymmetric broadcast channel. Thus, this outer bound is in general tighter than all other known outer bounds on the discrete memoryless broadcast channel. We consider a discrete memoryless (DM) broadcast channel where the sender wishes to communicate common as well as separate messages to two receivers [2] . Formally, the channel consists of an input alphabet X , output alphabets Y and Z, and a probability transition function p(y, z|x). A ((2 nR0 , 2 nR1 , 2 nR2 ), n) code for this channel consists of (i) three messages (M 0 , M 1 , M 2 ) uniformly distributed over [ . The probability of error is defined as. A rate tuple (R 0 , R 1 , R 2 ) is said to be achievable if there exists a sequence of ((2 nR 0 , 2 nR 1 , 2 nR 2 ), n) codes with P (n) e → 0. The capacity region of the broadcast channel is the closure of the set of achievable rates.. The capacity region for this channel is known only for some classes, including the degraded, less noisy, more capable, and semi-deterministic channels (see [4] for a survey of these results). Additionally, general inner bounds by Cover [3] , van der Meulen [9] and Marton [7] and outer bounds by Körner and Marton [7] and Sato [8] have been established. Furthermore, the Körner and Marton [7] outer bound is known to be tight for all cases where capacity is known.. In this paper we introduce an outer bound on the capacity region of the DM broadcast channel based on results in [5] and show that it is strictly tighter than existing outer bounds. The outer bound is presented in the next section. In Section 3, the outer bound is specialized to the case of no common information. In Section 3-C, we show that when there is no common information, our outer bound is contained in the Körner-Marton bound and in Section 4 we show that this containment is strict. We presented an outer bound on the capacity region of the DM broadcast channel, which is tight for all the special cases where the capacity is known. We showed that the bound is strictly smaller than the Körner-Marton for the BSS channel. The outer bound, however, is strictly larger than the Cover-van der Meulen region for this channel. We believe that the outer bound given by equation (3.1) is strictly tighter than the one given in Theorem 3.1 for the BSSC.

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The automatic solution of partial differential equations using a global spectral method Abstract. A spectral method for solving linear partial differential equations (PDEs) with variable coefficients and general boundary conditions defined on rectangular domains is described, based on separable representations of partial differential operators and the one-dimensional ultraspherical spectral method. If a partial differential operator is of splitting rank 2, such as the operator associated with Poisson or Helmholtz, the corresponding PDE is solved via a generalized Sylvester matrix equation, and a bivariate polynomial approximation of the solution of degree (nx, ny) is computed in O((nxny) 3/2 ) operations. Partial differential operators of splitting rank ≥ 3 are solved via a linear system involving a block-banded matrix in O(min(n 3 x ny, nxn 3 y )) operations. Numerical examples demonstrate the applicability of our 2D spectral method to a broad class of PDEs, which includes elliptic and dispersive time-evolution equations. The resulting PDE solver is written in Matlab and is publicly available as part of Chebfun. It can resolve solutions requiring over a million degrees of freedom in under 60 seconds. An experimental implementation in the Julia language can currently perform the same solve in 10 seconds.

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Multivalent functions with respect to n-ply points and symmetric conjugate points Multivalent analytic functions that are starlike, convex, close-to-convex, and quasi-convex with respect to n-ply points as well as multivalent functions with respect to symmetric points, conjugate points and symmetric conjugate points are considered. These classes of functions interestingly unify various known subclasses that have separately been introduced and studied. Inclusion and convolution properties of these generalized classes are investigated, and numerous known results could be obtained as special instances.

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Diploid versus Haploid Organisms Using a bit string model, we show that asexual reproduction for diploids is more efficient than for haploids: it improves genetic material producing new individuals with less deleterious mutations. We also see that in a system where competition is present, diploids dominate, even though we consider some dominant loci. In a system where population is kept constant, competiton just allows the best individuals to survive, i.e. individuals which have less deleterious mutations and are better adapted to the current enviroment have a higher probability to survive. Haploid gametes reproduce by mitosis, mutations being the only source of variability, thus they cannot improve too much when reproducing. On the other hand, diploids could reproduce with crossings between two equivalent copies of genetic material, by meiotic parthenogenesis. This is supposed to be a more efficient way to improve the ability to adapt the population to the environment. The purpose of this work is to verify this hypothesis within a simple bit string model [1] of population dynamics.. The hypothesis of Jan, Stauffer and Moseley (JSM) [2] proposes that haploid populations convert first into asexual diploid and then diploids begin to reproduce in a sexual way. This complete process has been studied in references [3, 4] , although they used a completely different model, with conversions (haploid → diploid and asexual → sexual) happening only for individuals with many deleterious mutations. In this case, sexual diploids finally dominate the system with a small fraction of asexual diploids and haploids.. In this work we show that in a system under competition, with an asexual population completely haploid at the beginning, and where with a small probability two haploids can fuse to create a diploid, evolution leads to a population dominated by diploids at the end. We also show that even the introduction of some dominant loci into the genetic material of diploids does not change too much this final result. In order to study the most efficient kind of behaviour (haploid versus diploid), we considered systems where competiton allows to survive only a pre-determined number of individuals. Of course, these selected individuals will be the best adapted to the environment, presenting a small number of deleterious mutations. Diploid reproduction is more efficient to improve new generations than haploid reproduction. The final population is dominated by diploids, and haploids are present only if we let some diploids to become haploids.. The presence of some dominant loci in diploids does not change the results. In the limiting case P DH = 0 (no diploid returns back to haploid condition), dominance just modifies a little bit the equilibration time, but eventually we have no haploids anyway.

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Supporting effective user navigation in digital documents Electronic documents such as PDFs are becoming increasingly popular as we move further towards the notion of the paperless office. The harsh truth however is that e-documents differ greatly from their physical paper counterparts, with many users opting to print them before reading. This paper describes several novel implementations that utilize a technique known as 'lightweight interaction'; a term that describes activities that can be performed without excessive cognitive attention. Incorporating tools into digital document readers to aid users in day-to-day tasks will enhance their performance and hopefully increase user uptake of digital reading. My research on this topic centers on several areas of document navigation, focusing specifically on current physical (paper) practices, in order to enhance their digital equivalents. With the introduction of the personal computer came predictions of the 'paper-less office' [1, 3] promising the redundancy of paper from everyday office related tasks. However, after more than 20 years [1] and despite the wide-spread availability of digital documents, consumer paper usage is increasing. The main problem with the digital document paradigm is the differences they pose from their physical paper counterparts [3] . The tangible properties of paper (e.g. that it's light, thin and flexible) afford many actions that are not possible on digital equivalents. Activities such as folding, ripping and flicking all contribute to the ease in which physical documents are manipulated and are difficult to replicate in digital media.. A useful way of encapsulating the affordances that paper documents offer is by 'lightweight navigation'; a term coined by Catherine C. Marshall in 2005 [2] . Marshall defines the term 'lightweight' as navigation that occurs either when people reach a particular page or when they move within an article in a way that is so unselfconscious that they aren't apt to remember it later. Although this type of activity is common in physical documents, it is rarely seen in digital navigation. Marshall speculates however, that this concept of 'lightweight' interaction can be also be applied to digital technology, but does not give any concrete evidence to support it. The goal of my PhD then, is to prove by example that lightweight interaction is indeed possible on the digital level and to consequently define the digital equivalent of the term.. There are many aspects of computerized technology that far exceed the capabilities of paper (i.e. searching, zooming etc) and by paying closer attention to the possibility of 'lightweight' navigation (and also its corollary: 'heavyweight'), digital document software can not only incorporate the physical affordances of paper but also improve upon it, by surpassing its limitations. It is for this reason that I feel this work is an original contribution to the field of HCI.

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Detecting image spam using visual features and near duplicate detection Email spam is a much studied topic, but even though current email spam detecting software has been gaining a competitive edge against text based email spam, new advances in spam generation have posed a new challenge: image-based spam. Image based spam is email which includes embedded images containing the spam messages, but in binary format. In this paper, we study the characteristics of image spam to propose two solutions for detecting image-based spam, while drawing a comparison with the existing techniques. The first solution, which uses the visual features for classification, offers an accuracy of about 98%, i.e. an improvement of at least 6% compared to existing solutions. SVMs (Support Vector Machines) are used to train classifiers using judiciously decided color, texture and shape features. The second solution offers a novel approach for near duplication detection in images. It involves clustering of image GMMs (Gaussian Mixture Models) based on the Agglomerative Information Bottleneck (AIB) principle, using Jensen-Shannon divergence (JS) as the distance measure. Email spam has been around for more than a decade, with many covert companies offering mass emailing services for product advertisements. It has become usual for people to receive more spam email than relevant email. The design of the SMTP protocol is one main reason for email spam: it is possible to send millions of fake email messages via computer software without any authentication. Lately, authentication policies have been adopted by various web servers, where webmasters can maintain a list of blocked web domains. However, spammers keep registering millions of domain names at a pace far greater than what system administrators can cope up with.. The biggest step in the fight against spam has been the successful design of spam filtering software. Such software typically trains classifiers to identify spam and ham. For the past decade, much progress has been made in designing accurate classifiers for text emails. These classifiers are trained to use two kinds of rules: (a), rules based on connection and relay properties of the email, and (b), rules exploiting the features extracted from the content of emails. The former category of rules utilize publicly shared blacklists of servers which have been known to transmit spam emails. This information is frequently updated, requiring spammers to change domain names and servers frequently. The second type of rules use features extracted from spam text (e.g. keywords, frequent words etc) which are compared with corresponding features from ham text (normal emails) and classifiers are learnt [6] . Such classifiers have proven to be very useful and highly accurate, even though spammers continue to devise novel ways of fooling them. In this paper, we have presented two novel approaches to fight the menace of image-based email spam by exploiting visual features and the template-based nature of I-spam. Experimental results heavily support our hypothesis that low-level feature analysis can provide a more accurate detection mechanism than previously known. Our work complements earlier work, and it should be very easy to incorporate visual features extracted by us into the framework of [5] . The gain is likely to be better accuracy, with a much improved running time by using the JIT feature extraction suggested by them. Since research in Ispam is recent, with less than one year since the emergence of the problem, more work is likely to happen in the future. In particular, spammers will notice the anti-spam measures taken and innovate to produce new attacks. The emergence of PDF based spam is one such innovation from spammers; clearly, spammers try to exploit all popular document formats to get their messages through. Till more principled shifts in email (e.g. postage for email c.a. [11] ) or improvements in the underlying protocols happen, antispam research will remain a firefighting operation. We first discuss the more general approach of binary classification using visual features. We use our homegrown ham data set consisting of images, photographs, logos, wallpapers, emoticons and similar items used in real world which has not been released in its entirety due to privacy reasons). We first resize the images to 100 × 100 pixels; this simple mechanism makes our method robust to random pixels, simple translation and scaling. It also helps greatly with computational requirements as texture and shape extraction are time consuming non-linear routines. We then extract features from all the images from the positive and negative test set. A fraction α of the images are used for training the classifier based on SVM light while the tests are then carried out on the remaining (1-α) fraction of images. Fig. 4 reports the prediction accuracy over the positive and negative set as function of α.. We observe that visual features are highly indicative of spam images; our method reports a prediction accuracy of over 95% in all cases. Even with large sets of over 15, 000 emails with only 10% data used for training, the prediction accuracy is higher than best numbers reported by [5, 6] . At a comparable fraction of training/test set as used by Dredze et al., we achieved almost 98% accuracy, an improvement of over 6%. With the Personal spam dataset, we achieve comparable results as [5] .. We also investigated the impact of resolution on our approach; in particular we are interested to know if lower resolutions can provide similar results at a cheaper computationally cost. Fig. 3 shows the results of this evaluation; we notice small losses at lower resolutions and small gains at higher resolutions. At 400 × 400, the approach is near perfect achieving more than 99.6%. Higher accuracies will be practically impossible due to human errors in labeling data.. It is important to point out that our results are nearly completely comparable with the previous papers; all researchers have used their own ham datasets, including us. The choice of ham can have a big impact on the outcome of prediction. However, the important issue is labeling spam correctly over a large heterogeneous spam set. We are currently procuring more spam by creating honeypots with an aim of collecting over 100,000 spam emails. We are also working on the creation of a SpamAssassin 5 Plug-in using our framework.. Results from GMM based AIB The Labeled AIB approach is unlikely to reach the same generalization performance; the approach is designed to identify permutations of a base template. Since positive images do not necessary follow this pattern and are potentially infinite, Labeled AIB is expected to be clueless about images previously unseen. Early experiments in settings as above lead to weak results. However, the idea is still powerful; collaborative approaches to spam filtering like Spamato [1] encourage users to share manually-classified email spam, which can be used to train 5 www.spamassasin.org the spam filters of other users in the community. Our approach fits very well into this framework, since AIB based on GMMs can identify the base template. This makes our approach also well suited to server deployment as an identified pattern can be protected against for a large user population before spam reaches individual inboxes. The goal is to detect a new pattern in a manner similar to viruses and make the pattern available to all subscribed email servers. Further investigation of this idea is in progress.. To explore how effective the GMM approach is in identifying patterns pre-classified as spam, we find the Princeton dataset very helpful. This dataset has various categories of I-spam images clustered according to the base template; descriptions of different strategies used by spammers are also provided. Fig. 6 demonstrates that two spam images produced by the same template are indeed modeled very similarly to each other.. Notice how well our chosen features can model the low resolution thumbnails with a simple parametric model. This provides the perfect opportunity to raise the question: Can AIB Clustering based on GMM identify the clusters correctly and demonstrate that it recognizes the base templates? We performed a run with our algorithm to test this hypothesis.. The Princeton dataset contains images in 178 categories, however this categorization is very strict. Many of these clusters can be merged and manual clustering resulted in fewer than 150 clusters. Our approach found 140 clusters; clearly some misclassification was also found. However, the performance is better than indicated by the numbers; clusters that were wrongly merged were classified as wrong, though they are similar when manually observed. When constrained to find exactly 178 clusters, the misclassification rate falls to below 16%. This indicates that the predictive performance for spam detection should still be high. Fig. 5 proves this empirically. GMM based Labeled AIB has high accuracy when predicting spam in comparable conditions. OCR does much worse, while the trained SVM has a marginally better performance.

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