Sentiment cascades in the 15M movement Recent grassroots movements have suggested that online social networks might play a key role in their organization, as adherents have a fast, many-to-many, communication channel to help coordinate their mobilization. The structure and dynamics of the networks constructed from the digital traces of protesters have been analyzed to some extent recently. However, less effort has been devoted to the analysis of the semantic content of messages exchanged during the protest. Using the data obtained from a microblogging service during the brewing and active phases of the 15M movement in Spain, we perform the first large scale test of theories on collective emotions and social interaction in collective actions. Our findings show that activity and information cascades in the movement are larger in the presence of negative collective emotions and when users express themselves in terms related to social content. At the level of individual participants, our results show that their social integration in the movement, as measured through social network metrics, increases with their level of engagement and of expression of negativity. Our findings show that non-rational factors play a role in the formation and activity of social movements through online media, having important consequences for viral spreading. The Occupy and M movements are recent examples of self-organized social movements that appeared in developed countries in response to a widespread perception of social and economical inequality [, ] . While these movements address a wide range of problems in different countries, they share a common factor, their usage of social media to communicate, organize, and deliberate about the purpose of the movement and its actions [, ] . Social media allow the participants of these movements to circumvent their lack of influence on state-and private-owned mass media [] , creating an emergent structure without a central actor or decision group. As a side effect, these movements leave public digital traces of their activity, which allows us to analyze their formation, behavior, and organization up to unprecedented scales and resolutions.. Collective actions pose a classical paradox of the tragedy of the commons []: A purely rational individual would choose not to participate in a movement it agrees with, as it would receive its collective benefits without the associated costs and risks of taking part on it. Thus, the existence of collective actions and social movements requires considerations beyond rational decisions, including emotions [] and social influence [] between the participants of a social movement. In this article, we present a detailed quantitative analysis of the digital traces of the M movement, the Spanish precursor of Occupy movements across Europe and America [] . This decentralized movement emerged in Spain in the aftermath of the so-called Arab Spring as a reaction to public spending cuts and the economic crisis. It was mainly nucleated in online social networks before massive offline demonstrations ended up in several camp sites in many city squares. From that point on, the movement consolidated and lasted for months. Even today the foundations of the M movement drives the political agenda of some new parties and associations in Spain.. Our analysis covers its online social structure and the content of the public messages exchanged in Twitter, the main online medium used by the movement. Twitter users create directed links to follow the messages of other users and communicate through short public messages called tweets. We analyze the content of a large set of tweets about the M movement, extracting sentiment values and semantic content related to social and cognitive processes. Our aim is to explore the role of social emotions in group activity and collective action. We address how emotional interaction supports the creation of social movements and how emotional expressions lead to the involvement of the participants of the movement.. According to the theory of collective identity of Emile Durkheim, group gatherings contribute to the creation of collective identity by means of rituals and symbols that produce an atmosphere of emotional synchrony [] . These rituals are often emotionally charged and show an inverse relation between emotional intensity and frequency [] . The emotions experienced by the participants of these gatherings contribute to social inclusion and identification with the collective, as empirically shown in a variety of experiments [] . This also holds for the M movement, for which survey results show that participants of the large demonstrations across Spain in  felt a stronger emotional communion with the movement, in comparison to those participants who did not attend to the demonstration [] . In this article, we provide a quantitative analysis of how collective identity and action emerged in the M movement, through the analysis of the digital traces of its participants in the Twitter social network. We pay special attention to emotional expression in tweets, social inclusion in the follower network of the participants of the movement, and sentiment polarization in the creation and social response to the movement.. Online media offer large datasets to explore political activity at a large scale, to find out about popularity and mobilization in political campaigns [] , and political alignment based on public messages [, ]. Analyzing online social networks, for example by means of the k-core decomposition method, can also reveal relevant information about the role of influential individuals [, ] and the social resilience of an online community [] . Users of online social networks communicate through public messages that provide the breeding ground for collective emotional states, which have the potential to create the identity and mobilization of the movement. Previous states of collective emotional persistence were detected in the short messages of IRC chats [] and spread through social networks as cascades of emotions [] , forming patterns in which happy individuals are likely to be connected to other happy ones [] .. In our analysis we follow a top-down approach, from the collective level of the movement to the actions of its individuals and their relations. We start by analyzing the dynamic aspect of the M movement, identifying cascades of tweets as in previous research [] . We measure the size of these cascades in terms of the amount of participants communi-cating in the cascade (spreaders), and the amount of participants exposed to the cascade (listeners). We analyze how cascade sizes depend on collective emotions and the use of terms related to cognitive and social processes. Finally, we zoom into the microscopic level of individuals and their interactions, creating an additional dataset of tweets of each participant of the M movement. We relate their expression of emotions, cognitive, and social processes to their activity and social integration in the movement, as quantified by their k-core centrality within the social network. The present work analyzes the evolution of the M movement through sentiment and linguistic analysis of the participants' communication in the Twitter social network. Using a dataset of tweets related to the M movement, we track the activity of , Twitter users. Our analysis includes , tweets during a period of  days, providing an illustration of the structure of the movement in two ways: (i) at the dynamic aspect of cascades in the discussion between connected users, and (ii) at the individual level of social integration and participation of each user. We combine psycholiguistics, sentiment analysis, and dynamic cascade analysis, to understand the role of tweet content in the size and reach of collective discussions in Twitter. In line with previous works in social psychology [], we assess the role of emotions in social interaction and collective action. We test the hypothesis that collective emotions fuel social interaction by analyzing cascades according to their emotional, cognitive, and social content. We find that the sentiment expressed in the first tweet of a cascade does not significantly impact the size of the cascade. Instead, the collective emotions in the cascade are responsible for its size in terms of spreaders and listeners. In particular, cascades without positive content tend to be larger, and their size follows a qualitatively different distribution. The cognitive content of the tweets of a cascade play no role in their spread. On the other hand, our analysis of social content in the cascades reveals a clear pattern: cascades with large ratios of social-related terms have distributions of listener and spreader sizes that scale with system size, in contrast with cascades with low ratios of social-related terms, which follow distributions that have bounded means. Our analysis at the individual level reveals that users are more integrated in the movement, measured by their k-core centrality, if they exhibit higher levels of engagement and express stronger negativity, in line with the overall negative context of the movement (indignants). Our analysis also reveals that highly integrated and influential users have a lower tendency to express social content in tweets. This indicates that social activation became salient in the periphery of the movement rather than in its core. We emphasize that our findings are consistent with theories in sociology and social psychology and confirm their statements by quantifying, for the first time, social and psychological influence in collective action at large scale.. Our results have implications for research on social movements. The differentiation between social and cognitive processes is evident when analyzing the size of cascades. Larger cascades have higher amounts of social terms, invoking the participation of other users. This is also consistent with our findings at the individual level: social integration is clearly related to activity levels, showing the relevance of nonrational factors in collective action. The members of a movement are not deterministically defined by their demographic background and income. Instead, the amount of social connections they have in the movement and their synchrony with the emotions expressed by the movement as a whole are predictors of their involvement.. Our findings show the added value of including additional psycholinguistic classes into our analysis, i.e. the consideration of social and cognitive terms beyond sentiment analysis. Furthermore, our sentiment analysis adaptation to Spanish demonstrates the relevance of sentiment analysis in languages other than English, offering new opportunities to compare collective phenomena in a wide variety of societies and political systems.. Beyond social movement analysis, our work has implications for studying other online phenomena, such as memes or viral marketing campaigns. Our findings on cascade sizes for different psycholinguistic classes suggest that words related to social processes lead to larger collective responses in Twitter, pushing the virality of content above a critical threshold that produces qualitatively different cascading behavior.

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


Towards a global control strategy for induction motor: Speed regulation, flux optimization and power factor correction A great deal of interest has been paid to induction machine control over the last years. However, most previous works have focused on the speed/flux/torque regulation supposing the machine magnetic circuit to be linear and ignoring the machine power conversion equipments. The point is that speed regulation cannot be ensured in optimal efficiency conditions, for a wide range of speed-set-point and load torque, unless the magnetic circuit nonlinearity is explicitly accounted for in the motor model. On the other hand, the negligence of the power conversion equipments makes it impossible to deal properly with the harmonic pollution issue due to 'motor -power supply grid' interaction. This paper presents a theoretical framework for a global control strategy of the induction machine and related power equipments. The proposed strategy involves a multi-loop nonlinear adaptive controller designed to meet the three main control objectives, i.e. tight speed regulation for a wide range speed-reference variation, flux optimization for energy consumption and power factor correction (PFC). Tools from the averaging theory are resorted to formally describe the control performances.Crown Induction motors are featured by their interesting power/mass ratio, relatively low cost and simple maintenance (as they include no mechanical commutator). It is widely agreed that these machines have promising perspectives in the industrial actuator field. This has motivated an intensive research activity on induction machine control especially over the last fifteen years. The complexity of this problem is threefold:. -The multivariable and nonlinear nature of the machine dynamics. -The multiform interaction with its environment: supply grid, power converters, varying load, etc. -The multiplicity of control objectives: speed regulation, energy consumption optimization, power factor correction, fault detection and diagnostic, etc.. Most previous works have focused only on speed/flux regulation (with constant flux reference) following several control strategies ranging from simple techniques, e.g. field-oriented control [11, 14] , to more sophisticated nonlinear approaches, e.g. feedback linearization [1] , direct torque control [21, 4] or sliding mode control [19] . A common point of these works is that the control design relies on a relatively simple machine model, next called standard model, assuming a linear representation of the magnetic circuit (which of course is not true in real machines). Accordingly, most previous control solutions involved flux regulation around constant values (Fig. 1) . Specifically, the constant flux reference is equal to its nominal value generally located at the elbow of the machine magnetic characteristic [12, 2] . Then, energetic efficiency is actually maximal provided that the machine operates all time in the neighborhood of its nominal point. But, this is not the case in most practical applications because the machine load is generally varying [8] . Indeed, in presence of small loads, the operation point is below the nominal value causing useless energy stored in stator inductances which reduces the machine efficiency. In the case of overloaded machine, this operates in the saturation zone of its magnetic characteristic but, then, the standard model is no longer valid and, consequently, the control performances are no longer guaranteed. To overcome the above shortcomings, it is necessary in speed control to let the flux reference be dependent on both the speed reference and torque-load, i.e. the flux reference must be state-dependent. Examples of speed/flux controllers involving state-dependent flux reference (Fig. 2) have been developed in [15] . The proposed controllers include optimal flux generators the design of which relies on a machine model that takes into account the nonlinearity of the magnetic characteristic.. However, even in the preceding works the control problem has been relatively simplified because the motor is viewed there as a

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


EXPLORATORY MODELING OF YEAST STRESS RESPONSE AND ITS REGULATION WITH gCCA AND ASSOCIATIVE CLUSTERING We model dependencies between m multivariate continuous-valued information sources by a combination of (i) a generalized canonical correlations analysis (gCCA) to reduce dimensionality while preserving dependencies in m − 1 of them, and (ii) summarizing dependencies with the remaining one by associative clustering. This new combination of methods avoids multiway associative clustering which would require a multiway contingency table and hence suffer from curse of dimensionality of the table. The method is applied to summarizing properties of yeast stress by searching for dependencies (commonalities) between expression of genes of baker's yeast Saccharomyces cerevisiae in various stressful treatments, and summarizing stress regulation by finally adding data about transcription factor binding sites. Integration of multiple information sources is becoming increasingly important in bioinformatics. It has become evident that most of the important questions in molecular and cell biology cannot be answered by studying a single data source, like DNA sequence or gene expression. Due to complexity and noise of biological systems, either lots of prior knowledge or data are required to constrain models, and single sources of measurements then suffice only for producing general overviews. In contrast, approaches that combine several relevant data sources have shown potential to answer specific biological questions. 4, 22 However, the works integrating heterogeneous data sets presented so far tend to be tailored to specific tasks. Their application to other problems is usually possible only after tedious tailoring by both methodological and application area experts. Typical examples of these methods are Bayes nets 7 and inductive logic programming. 17 We investigate machine learning methods that can both integrate multiple information sources and at the same time are generally applicable to a set of problems of a certain kind. We assume that the multiple data sets are multivariate and continuousvalued. The problem is to find what is common in the data sets, commonality being defined as such properties of the data sets that are consistently (statistically) dependent. The question is then how to utilize this kind of multiple information in a principled way to search for dependencies with minimal assumptions about their nature. Specifically, we study yeast stress response on gene expression level, and its regulation by a set of proteins called transcription factors. We have demonstrated the effectiveness of exploratory dependency modeling for characterizing yeast gene expression under stress, and its regulation.. We applied generalized canonical correlations (gCCA) in a novel way to multiple stress expression sets to produce one representation for the sets, which preserves mutual information between the sets. This preprocessed data was then clustered with AC to maximize its dependency with binding profiles of a set of regulators.. Biological relevance of the clusters was confirmed with several tests and database searches. We can conclude that our approach succeeded notably well both in confirming some known facts, and in generating new hypotheses.. From the technical point of view, the main benefit from gCCA in this work was the reduction of the multiple data sets into one representation, in a mutual information-preserving way. However, the resulting two stage approach is naturally suboptimal, since combining the projection and clustering into one method should improve the results. This is one possible future research direction.. Another interesting future research direction is to use a kernel version of gCCA 24, 1 in combining the data sets. In principle, the nonlinear kernel CCA is superior in finding the correlating components when the data is not normally distributed. However, further work will be required to study the regularization of the kernel CCA, the choice of kernel function, its application in the integration of the data sets, and its interpretation. There exist additionally other promising kernel-based data integration methods in bioinformatics.

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


Discovering the core semantics of event from social media As social media is opening up such as Twitter and Sina Weibo 1 , large volumes of short texts are flooding on the Web. The ocean of short texts dilutes the limited core semantics of event in cyberspace by redundancy, noises and irrelevant content on the web, which make it difficult to discover the core semantics of event. The major challenges include how to efficiently learn the semantic association distribution by small-scale association relations and how to maximize the coverage of the semantic association distribution by the minimum number of redundancy-free short texts. To solve the above issues, we explore a Markov random field based method for discovering the core semantics of event. This method makes semantics collaborative computation for learning association relation distribution and makes information gradient computation for discovering k redundancy-free texts as the core semantics of event. We evaluate our method by comparing with two state-of-the-art methods on the TAC dataset and the microblog dataset. The results show our method outperforms other methods in extracting core semantics accurately and efficiently. The proposed method can be applied to short text automatic generation, event discovery and summarization for big data analysis. With booming social media, the data explosion of microblog on blogosphere accompanies with hot events. For example, a large volume of microblogs discussed about "USA Boston Marathon bombing", "the US surveillance program PRISM " and so on. Given the microblogs/short texts 2 about a concrete event, 5 information about the event is unevenly distributed on these "event messages" since some ones might contain much more important and diverse information (e.g., different event time, locations, participants, processes, and opinions) than others (e.g., redundancy and noises in short texts). Besides, these short texts are globally semantic redundant and locally semantic sparse since many short 10 texts contain the same content and local ones only focus on one aspect of the whole event. Understanding the event concisely and thoroughly is impossible when these redundant short texts may crowd out other ones which contain important and diverse information. For example, when we query by keyword "Ebola", Sina Weibo returns redundant Chinese microblogs about "Ebora of 15 salmon" and Twitter returns redundant tweets about "A Italian doctor catches Ebola" as shown in Fig.1 , which crowd out many important microblogs which discuss about outbreak, spreading and control of event "Ebola".. Herein, how to automatically discover the core semantics of event from big social media data is a challenging problem, since it is time-consuming and un-20 practical to manually find out the core semantics of event from big media data.. Existing methods to solve this problem are summarized as follows: 1) Feature-based methods. These methods directly use basic statistic technique on features including word frequency, title words, cure words which are considered for selecting sentences as core semantics [1, 2] . Structural features of discourse are used to identify core semantics by rhetorical structure analysis, pragmatic analysis, lexical chain, latent semantic analysis [3] . Besides, more features are used in some specified semantics discovery methods whose features include hashtags, timestamps and emotion labels [4] .. 2) graph-based methods. These methods construct graph where short texts as 30 nodes and text-pairwise relations as edges [5, 6, 7, 8] . Top k short texts are selected as core semantics by ranking values of graph-based features or values of Markov random walk on the graph [5, 6, 7, 8] . Besides, such methods can be extended into conditional random fields which identify core semantics by labeling sentences, where the sentence label influences the labels of nearby 35 sentences [9] .. 3) clustering-based methods. These methods cluster short texts into different clusters, and then select some short texts from each cluster to represent the semantics of the cluster [10, 11, 12] . The clustering methods include hierarchical clustering, partitional clustering and semantic-based clustering [13] . 40 Besides, some priori knowledge or constraint conditions in specified domain are considered in clustering [14, 15] . 4 ) semantic link-based method. Semantic link-based methods have strong abil-ities in semantics organization, semantic community discovery and emerging semantics learning/reasoning [16] . Such methods have been used in semantic 45 representation[17], semantic organization [18] , semantic interaction [19, 20] , semantic community discovery [21, 22] and semantic linking space for Cyber-Physical Society [23, 20] . 5) other methods. These methods include Bayesian topic model-based methods [24] , Neural Networks-based methods, Decision tree-based methods and so on[25,. However, these methods have the following limitations: 1) semantic association loss. The graph-based and cluster-based methods often use vector space model to represent short texts and use vector-based similarity methods. Obviously, these similarity-based methods lost many semantic 55 association relations;. 2) high computational cost. The time complexity of most the above methods [5, 6, 7, 8, 10, 11, 12] , which have to compute text-pairwise similarity, is O(n 2 ). It is unpractical when the text number is large in big data;. 3) redundancy-prone results. The above methods pay less attention on the issue 60 of redundancy and result in redundant results since these methods assign almost the same values to alike short texts.. To solve the above limitations, we propose a Markov random field based method for discovering the core semantics of event:. 1) To avoid semantic association loss, our method makes semantic collaborative 65 computation to learn the whole association relation distribution of an event by small-scale association relations .. 2) To reduce computation cost, our method makes probabilistic inference in a limited keyword association link network, rather than text-pairwise computation. 3) To be free of redundancy, our method proposes information gradient computation by maximizing information gradient of k short texts since information gradient decreases when redundancy increases.. Compared with existing methods, the contributions of our method are summarized as follows: 75 1) Our method learns association relation distribution by semantic collaborative computation.. 2) Our method is efficient by probabilistic inference on semantic association link network.. 3) Our method obtains redundancy-free core semantics by information gradient 80 computation.. The remainder of the paper is organized as follows. In Section 2, we introduce the preliminaries including some basic definitions and problem formal definition.. In Section 3, we propose a framework of Markov random field based method for discovering the core semantics of event. We construct a Markov random field 85 by semantic association collaborative computation, which learns association relation distribution by low-degree relations in Section 4. We propose information gradient computation to maximize coverage of association distribution by the minimum number of redundant-free short texts in Section 5. Experimental results are presented in Section 6. We gives the conclusion and future work in 90 Section 7. In this section, we conduct some experiments to validate the correctness of 330 our method. For evaluation of our method, we use dataset 1 and dataset 2 to conduct the experiments. The sentences in dataset 1 and dataset 2 are tokenized and stemmed by Stanford parser tools 5 . Our method for discovering core semantics is conducted as follows: 2) To solve problem of maximizing information gradient, we select k sentence 375 as the core semantics of event by algorithm 3 in section 5. Dividing information gradient by the maximum value, we normalizes the 390 information gradient. Fig.5 shows the normalized information gradient of k th sentence(1 ≤ k ≤ 30). It shows that the value of information gradient decreases from 1 to a stable value which is approaching to 0. As more sentences are selected as core semantics of an event, the incoming sentence contains lower information gradient since most semantic association relations of the event have 395 been described. The most of semantic association is covered by the first 15 sentences and the information gradient of remains sentences is extremely weak.

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


Decoupled Linear Estimation of Affine Geometric Deformations and Nonlinear Intensity Transformations of Images Abstract-We consider the problem of registering two observations on an arbitrary object, where the two are related by a geometric affine transformation of their coordinate systems, and by a nonlinear mapping of their intensities. More generally, the framework is that of jointly estimating the geometric and radiometric deformations relating two observations on the same object. We show that the original high-dimensional, nonlinear, and nonconvex search problem of simultaneously recovering the geometric and radiometric deformations can be represented by an equivalent sequence of two linear systems. A solution of this sequence yields an exact, explicit, and efficient solution to the joint estimation problem. We have proposed a novel method for jointly estimating the geometric and radiometric deformations relating two observations on the same object. The case of a geometric affine transformation relating the images' coordinate systems, and a nonlinear function relating the intensities of the two images has been modeled.. In Section 3, we derived an estimation method for the joint problem. We have shown that, by using the sample distribution transformation T , the joint estimation problem may be decoupled into two simpler problems in the unknown radiometry and unknown geometry. More specifically, we have shown that the original high-dimensional, nonlinear, nonconvex search problem of simultaneously recovering the geometric and radiometric deformations can be represented by an equivalent sequence of two linear systems. In the absence of noise, solution of this sequence yields an exact, explicit, and efficient solution to the joint estimation problem, regardless of the deformations' magnitudes. In a forthcoming paper, we further elaborate on the performance of the proposed method in the presence of model mismatches. (See [33] for a preliminary exposition of the extended solution.). Results obtained in experiments with real images show the applicability of the methods proposed in this paper. The proposed linear framework of the solution was shown to lead to an efficient method, of linear computational complexity, which is extremely robust to model mismatches. Thus, the proposed solution may be used as is, or in conjunction with other techniques in case improved performance is required; for example, estimation results may be refined by efficiently exploiting the robustness of the proposed method in order to initialize an optimization-based estimation method. Such an approach will considerably reduce the probability of wrong convergence due to local minima, as well as the involved computational load.. The principles of the proposed method, and the general framework into which the problem of joint estimation of geometric and radiometric deformations has been casted to, may be further exploited to address generalizations of the model described in this paper. These include the cases of an elastic (nonaffine) geometric deformation as well as the case of noninvertible intensity mappings.

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


Bootstrapping comparison on availability of parallel systems with non-identical components Purpose -In order to develop a feasible and efficient method to acquire the long-run availability of a parallel system with distribution-free up and down times, the purpose of this paper is to perform the simulation comparisons on the interval estimations of system availability using four bootstrapping methods. Design/methodology/approach -By using four bootstrap methods; standard bootstrap (SB) confidence interval, percentile bootstrap (PB) confidence interval, bias-corrected percentile bootstrap (BCPB) confidence interval, and bias-corrected and accelerated (BCa) confidence interval. A numerical simulation study is carried out in order to demonstrate performance of these proposed bootstrap confidence intervals. Especially, we investigate the accuracy of the four bootstrap confidence intervals by calculating the coverage percentage, the average length, and the relative coverage of confidence intervals. Findings -Among the four bootstrap confidence intervals, the PB method has the largest relative coverage in most situations. That is, the PB method is the best one made by practitioners who want to obtain an efficient interval estimation of availability. Originality/value -It is the first time that the relative coverage is introduced to evaluate the performance of estimation method, which is more efficient than the existing measures. To assess the long-term performance of a repairable system, the steady-state availability of the system is often considered. Steady-state availability (henceforth availability) is defined as the ratio:. where m 1 and m 2 denote the mean up and down times of system, respectively. Availability is a very important measure in evaluating the long-term performance of a system, and numerous studies have sought to explore interval estimations for the availability of repairable systems assuming various specified up and down time distributions. Confidence intervals for availability are given in Thompson (1966) , Gary and Lewis (1967) , Masters and Lewis (1987) , Elperin and Gertsbakh (1988) , Mi (1991) , Masters et al. (1992) , Chandrasekhar et al. (1994) , Ananda (1999 Ananda ( , 2003 , Yadavalli et al. (2002) , Chandrasekhar et al. (2004) , and Lim et al. (2004) , among others. Recently, Ke and Chu (2007) considered the interval estimation of the steady-state availability for a repairable system consisting of one operating unit and one spare. But to the best of our knowledge there has been no research that explores interval estimation of availability for a parallel system with distribution-free up and down times. The contributions made by some important articles, concerning interval estimation of availability for a parallel system, are summarized in Table I .. All the Bayesian, M-, L-, G-and G-L methods (Table I ) not only assumed exponential type distributions on up and down time of components in the parallel system, but are also assessed by coverage percentage. Their results are just theoretically perfect, but the applications are limited to some special systems. This motivates us to develop more useful interval estimation methods for the availability of a parallel system with distribution-free up and down times.. The parallel system is popularly used in practical system. So far very few researchers have studied the estimation of availability for a parallel system with distribution-free up and down times. In general, engineers urgently want to assess the availability of a system. Therefore, this would motivate that we study the availability of a parallel system using nonparametrically statistical methods. To propose an efficient estimation method to estimate the system availability, the performance of these methods is also evaluated by some statistical criterions.. The objective of this paper is to present bootstrapping interval estimation of availability A for a parallel system assuming distribution-free up and down times. In second section, we show that the natural estimatorÂ of A is consistent and asymptotically normal (CAN). Further utilizing four bootstrapping interval estimation methods, we can construct four bootstrap confidence intervals for A. Subsequently, a numerical simulation study is conducted in third section to demonstrate performance of the four bootstrap confidence intervals for A. We assess and compare the accuracy of these four bootstrap interval estimation methods by virtue of relative coverage of confidence intervals. Finally, some conclusions are drawn.. Interval estimation of availability for a parallel system Consider a parallel system having k independent renewable components with distribution-free up and down times. For the ith (i ¼ 1,2, . . . ,k) component in the system, we let X i and Y i denote independent up and down times with means m 1i and m 2i , respectively. Then the availability of the parallel system in equilibrium is given by:. Pðthe ith component is downÞ. We are interested in constructing confidence interval of A by utilizing bootstrap technique.. Assume that X i1 ; X i2 ; . This paper proposed four feasible and efficient interval estimations of availability A for a parallel system with distribution-free up and down times. Based on CAN estimatorÂ, the four bootstrap methods SB, PB, BCPB and BCa are applied to construct confidence intervals for system availability A. The relative coverage is adopted to understand, compare, and assess performance of the resulted bootstrap confidence intervals. The simulation results imply that the PB method has the best performance on relative coverage. Consequently, the PB method is the best one made by practitioners who want to obtain an efficient confidence interval of availability A for a practical parallel system. Note that the bootstrap estimation methods presented in this paper would be easily applied to practical parallel systems. Further research may consider comparison investigations of system characteristics for two or more parallel systems by means of bootstrap technique.

O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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


Understanding and forecasting hypoxia using machine learning algorithms This study's primary objective lies in short-term forecasting of where and when hypoxia may transpire to enable observing its effects in real time, focusing on a case study in Corpus Christi Bay (Texas). Dissolved oxygen levels in this bay can be characterized by three temporal trends (daily, seasonal, and long-term). To predict hypoxic events, these three mathematical trends are isolated and extracted to obtain unbiased forecasts using a sequential normalization approach.Next, machine learning algorithms are constructed employing the continuous, normalized values from a variety of sensor locations. By including latitude and longitude coordinates as additional variables, a spatial depiction of hypoxic conditions can be illustrated effectively, allowing for more efficient summer data collection and more accurate, near-real-time projections. Using k-nearest neighbor and regression tree algorithms, approximate probabilities of observing hypoxia the following day were calculated, and estimates of dissolved oxygen levels were also computed.During periods in which hypoxia was observed, forecast probabilities of hypoxia exceeded 80%.Conversely, during periods in which no hypoxia was observed, the model's estimate remained below 20%. These results indicate that the modeling approach produces reasonable forecasts for this case study. Hypoxia is a condition in which aquatic dissolved oxygen (DO) levels drop to levels below 2 mg/L (Dauer et al. 1992) .. Generally, research has found hypoxia resulting from anthropogenic eutrophication (Campbell & Goodman 2007) , biological oxygen demand (Mallin et al. 2006) , and more specifically, the leaching of nitrates from fertilizers into local groundwater (Booth & Campbell 2007) .. Hypoxia has been observed in watersheds from the Great Lakes (Loewen et al. 2007) , to European lakes (Galkovskaya & Mityanina 2005) , and has spread considerably in marine ecosystems since the 1960s (Diaz & Rosenberg 2008) .. Corpus Christi Bay, Texas (USA) hypoxia results from a seasonal periodicity of dissolved oxygen fluctuations (Ritter & Montagna 1999) , a diurnal cycle resulting from tides (Hagy & Murrell 2007) or photosynthetic timing as nighttime oxygen consumption by fauna coincides with an absence of oxygen creation by flora (Goldshmid et al. 2004) , and salinity-induced stratification (Hodges & Furnans 2007b) . The analysis indicates that k-nearest neighbor algorithms can produce reasonable estimates of the probability of observing a hypoxic event the following day given inputs of 9-11), wind speed, and wind direction (shown to be meaningful in Figure 17(a,b) ) for the present bay status.. Hind-casting at other locations apart from sensor 24 could be useful to provide a basis of comparison-in the current study, sensor 24 was the only continuous sensor able to provide a sufficient stretch of data for this task. By implementing machine learning algorithms to isolate those variables most correlated with future dissolved oxygen levels, confirmations of hypotheses regarding the mechanisms that cause hypoxia in Corpus Christi Bay are aided. k-nearest-neighbor models provide insight into these mechanisms by displaying sensitivity to those factors which alter dissolved oxygen levels most. To improve understanding and create near-term forecasts of hypoxia in Corpus Christi Bay, this study uses a data mining approach. Data mining is a process for extracting knowledge from data, with the steps illustrated in Figure 3 . First, data are gathered, cleaned, and processed. Next, those data are transformed appropriately. Iterative algorithms are performed on those data and output is evaluated. At this point, the patterns obtained from mathematical analysis can become useful knowledge.. For the purposes of studying the occurrence of hypoxia, this data mining approach will require five steps. Before these five steps can take place, target data must be extracted (selection step in Figure 3 ). The dataset contains samples at various depths. The target data are bottom data, at continuous sensors only. For preprocessing and transformation, three separate temporal trends with regards to dissolved oxygen will need to be removed before meaningful comparisons can be made and defensible analysis can be performed. Analysis indicates that before these trends are extracted, over 25% of the total variance associated with our historical database of dissolved oxygen values was due solely to temporal factors. Normalization leaves transformed data (Figure 3 ) with which to continue the analyses.. Next, the data mining step is performed, having been assured that the remaining dissolved oxygen patterns are not time-dependent. Additional relevant variables such as salinity, pH, temperature, conductivity, etc. are examined to determine if they are useful in predicting dissolved oxygen levels. The machine learning models for real-time predictions First, a basic linear description of the long-term decrease in the average daily dissolved oxygen levels is proposed:. Note: In Equations (1) - (9), k 1 through k 12 are simply numerical constants which are obtained by minimizing the sum of squared residuals. For instance, in Equation (1), k 1 and k 2 are the constants from a linear regression. A linear model was selected over other exponential and logarithmic decay functions due to higher correlation coefficient values.. To remove this trend, the dataset is normalized to smooth the long-term decrease and remove cyclical functions. In Equations (2)- (9), n 1 , n 2 , and n 3 represent the number of dissolved oxygen points DO i .. The normalized DO values are then adjusted by comparing each data point to the value we might expect from that point in our historical dataset of continuous sensors (beginning in 1999), creating a DO time series which does not contain the long-term decay:. At this stage, the long-term trend is gone. Since the remaining trends are both periodic, modeling them via cyclical functions is the next step. By constructing a discrete. Fourier transform with two harmonics, capturing the annual periodicity within this adjusted data is now possible:. (t is measured in units of days). An analogous normalization (the first normalization removed the long-term trend while this second normalization removes the annual cycle) is then performed to generate yet another adjusted dataset, which lacks both a long-term trend and an annual oscillation:. Finally, to remove the only remaining cycle, the diurnal undulation, the following transformation is performed on the twice-adjusted data (i.e. the data with the long-term and seasonal trends removed):. From here, the diurnal relationship is calculated and a second discrete Fourier transform with two harmonics is performed:. (t is now measured in units of hours). Continuing:. DO thrice norm;t ¼ DO twice norm;t DO diurnal ðtÞ P n 3. At this point, normalization has removed three time dependencies. We verified this by re-analyzing the normalized data for trends; the normalized data exhibited minimal correlations with respect to time. Consequently, any value from any historical record can be compared to any other by adjusting thrice, as follows:. Isolating parameters that influence DO/model testing . For the case of hypoxia, this equation might appear as below: DO thrice -adjusted;t ¼ DO t. On the tenth day, we would not know the dissolved oxygen value for twenty-four hours into the future. Consequently, only the first nine days of the ten-day sample can become part of the training set.. In addition to the previous normalized values of dissolved oxygen, salinity, wind and temperature may also play a role in determining tomorrow's DO. As the body of knowledge expands, it is trivial to add additional terms, as in Equation (11) Calibration and validation using historical data at a single location. To calibrate the models described in the previous section at a single location, a sliding window approach is used.. A segment of data (the most recent segment) is first reserved for validation. In the Corpus Christi Bay case, the longestspanning readings from any continuous source are from sensor 24 during the summer of 2005. This will be the location used for hind-casting and model validation.

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


Evolutionary data-modelling of an innovative low reflective vertical quay Vertical walls are commonly used as berthing structures. However, conventional vertical quays may have serious technical and environmental problems, as they reflect almost all the energy of the incident waves, thus affecting operational conditions and structural strength. These drawbacks can be overcome by the use of low reflective structures, but for some instances no theoretical equations exist to determine the relationship between the reflection coefficient and parameters that affect the structural response. Therefore, this study tries to fill this gap by examining the wave reflection of an absorbing gravity wall by means of evolutionary polynomial regression, a hybrid evolutionary modelling paradigm that combines the best features of conventional numerical regression and genetic programming. The method implements a multi-modelling approach in which a multiobjective genetic algorithm is used to get optimal models in terms of parsimony of mathematical expressions and fitting to data. A database of physical laboratory observations is used to predict the reflection as a function of a set of variables that characterize wave conditions and structure features.The proposed modelling paradigm proved to be a useful tool for data analysis and is able to find feasible explicit models featured by an appreciable generalization performance.C. Altomare Wave action on natural environment and engineering structures can cause different kinds of problems that need to be predicted to prevent possible unexpected effects. Analysing the response of coastal structures to wave action is a very challenging task for engineers; in particular, a detailed characterization of the effects of quays or breakwaters is a key point for design, involving technical, economic and environmental aspects. However, it is not easy to achieve a holistic representation of the involved hydrodynamic phenomena, and in particular of wave reflection. To this aim, prototype measurements and physical laboratory tests can provide information on coastal structures and their behaviour, but most of the time it is difficult to characterize the main aspects of such phenomena. Recently, the increasing availability of different kind of data and information allows the application of innovative data-mining techniques to study and predict wave action and its effects (Zhang et The aim of this section is to discuss EPR-MOGA results from the modelling point of view, emphasizing its main features (for example, the ability to provide symbolic expressions, ranking of formulae according to their accuracy versus parsimony, etc.). As evidenced by Table 4 , the optimal solutions range from a very parsimonious model, the model in Equation (12), up to Equation (21) that conversely contains the higher number of explanatory variables.. Equation (12) is the simplest one, but presents a very low accuracy with respect to the others.. From models obtained, as listed in Table 4 , it is firstly possible to have an idea of the most used explanatory variables (i.e. dimensionless groups) for the phenomenon at stake. Therefore, it is possible to discuss the dimensionless group's importance:. with a positive exponent (i.e. direct dependence), except from Equation (19) where it appears also with negative exponent (at a second term).. • Eight equations (80%) show R* ¼ dL 0 2 /H s (0.5D 50 ) 2 with a positive exponent (i.e. direct dependence).. • Eight equations (80%) show s 0 ¼ H s /L 0 with a negative exponent (i.e. inverse dependence), with the exception of Equations (20) and (21) where it appears with a positive exponent (at a second term).. • Finally, I ¼ (d/gT p 2 ) 0.5 appears in seven equations (70%) with exponents both positive and negative.. Equation (12) is the simplest model, with only one constant and one dimensionless group, but it presents a very low accuracy. Equations (13) and (14) present only one constant, but with a combination of more than one variable. Both. Equations (13) and (14) and Equation (18) show a new group R*/s 0 . Equation (13) is a simple model but accurate, differently from Equation (14) where the presence of I. gives an inverse proportionality with the wave period, which is not physically consistent, resulting in a lower. CoD than Equation (13).. Models with more than one constant lead to a better accuracy than the simplest equations: generally one term is a function of the nominal diameter of the armour units D 50 , contained in R*, and the other term depends on the wave steepness and water depth.. A is present in several parsimonious models, showing an influence of the quantity (d-h c ) that represents the wet part of the chamber, thus responsible of wave energy dissipation due to the eddies among the armour units.. The exponents of R* are always positive: this result is physically sound because it implies a direct dependence of C r on the wave period and an inverse dependence on the nominal diameter. In fact a greater D 50 means higher porosity, and therefore higher dissipations leading to reduction in wave reflection.. The wide variation of the exponent for I suggests that this parameter is not significant, thus excluding models where it appears (as showed by the low accuracy of the Equation (12) It is necessary to try to make a choice among models in Table 4 , thus mimicking what could happen in real life.. When a researcher/operator is asked to define a good model to use in further applications, the selected model is Equation (13) as it represents a simple and accurate model, dependent on the main structural and hydraulic parameters.. Equation (13) In particular, the authors proposition is to use the model of Equation (13), which is a compact formula dependent on the main involved variables (i.e. water depth d, deepwater wave length L 0 , significant wave height H s , and nominal diameter of armour units D 50 ). However, the authors' advice is to employ this formulation inside the ranges for which it has been developed, considering the main dimensional variables summarized in Table 1 and carefully out of those ranges.

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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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


MAC-aware Routing Metrics for the Internet of Things Wireless medium access control (MAC) and routing protocols are fundamental building blocks of the Internet of Things (IoT). As new IoT networking standards are being proposed and different existing solutions patched, evaluating the end-toend performance of the network becomes challenging. Specific solutions designed to be beneficial, when stacked may have detrimental effects on the overall network performance. In this paper, an analysis of MAC and routing protocols for IoT is provided with focus on the IEEE 802.15.4 MAC and the IETF RPL standards. It is shown that existing routing metrics do not account for the complex interactions between MAC and routing, and thus novel metrics are proposed. This enables a protocol selection mechanism for selecting the routing option and adapting the MAC parameters, given specific performance constraints. Extensive analytical and experimental results show that the behavior of the MAC protocol can hurt the performance of the routing protocol and vice versa, unless these two are carefully optimized together by the proposed method.Keywords: Medium Access Control, Routing., Cross-layer interactions. * Corresponding authorEmail addresses: pidm@kth.se (P. Di Marco), georgioa@kth.se (G. Athanasiou), vmekikis@tsc.upc.edu (P.-V. Mekikis), carlofi@kth.se (C. Fischione) 1 P. Mekikis was with the School of Electrical Engineering, KTH, when contributing to this work. Internet of Things (IoT) has been introduced in 1999 to envision the concept of connecting physical objects, such as sensors and smart phones, supporting direct wireless connection to the internet. This enables an immense variety of applications in an Internet-like framework [1] . Any terminal connected to the Internet will be able to interact with these objects. Applications include among others, building and industrial automation, health-care, personal wireless communications, smart grids, and security. However, to support such an increasing number of emerging applications, the wireless medium access control (MAC) and the routing protocols must be inherently scalable, interoperable, and they must have a solid standardization base to support future innovations.. Differently from classic wireless ad hoc and sensor network applications, IoT applications have complex and heterogeneous requirements. The network performance is not only measured in terms of throughput, but other indicators, such as reliability, delay, and energy consumption, must be jointly optimized and adapted to different application scenarios [2] . Assuming a network of several objects interconnected to support an IoT application where efficient information routing is essential, the decision over different routing paths highly depends on link performance indicators, which are influenced by the MAC parameters. On the other side, the routing determines the distribution of the traffic load in the network, which significantly affects the aforementioned performance indicators. Therefore, it is inefficient to design MAC and routing protocols separately and their joint adaptation is essential to achieve the desired network performance. The essential question is which are the metrics that enable such an efficient joint adaptation.. By following a classic layered design process, standardization bodies such as IEEE and Internet Engineering Task Force (IETF) are working independently on the design of the future MAC and routing protocols for IoT. According to recent surveys [3] , the protocol stack for IoT applications integrates the IEEE 802.15.4 [4] standard for physical and MAC layers, and the IPv6 routing protocol for low power and lossy networks (RPL) [5] . The IEEE 802.15.4 defines flexible physical and MAC layers for low data rate and low power applications. The standard has been adopted with some modification also by a number of other solutions, including ZigBee, WirelessHART, ISA-100 [6] , and it already represents more than 50% of the building and industrial automation market [7] . At the networking layers, the IPv6 routing protocol for low power and lossy networks (RPL) represents the reference standard proposed by the IETF for IPv6-compatible IoT applications.. In this paper, we first emphasize the main characteristics of the IEEE 802. 15.4 MAC and the IETF RPL protocols. We then present an analysis to characterize the protocols mutual effects and their dynamics. In particular, we show that the level of contention at the MAC layer impacts the routing decisions in an unexpected manner. In addition, we show that in the presence of dominant paths in the network, i.e., paths with high traffic forwarding, the performance indicators at the MAC layer are significantly affected. Based on our analysis, we propose metrics that guide the interactions between MAC and routing, and we introduce a mechanism that selects the appropriate routing metric and adapts the corresponding MAC parameters to fulfill one of the most important requirement of several IoT applications: minimize the energy consumption in the network, given reliability and delay constraints.. Our methodology starts from the modeling of the behavior of the current protocols, motivating in this ways the design of new efficient metrics. The proposed metrics are numerically and experimentally validated in a realistic environment and in comparison to existing metrics. To the best of our knowledge this is the first paper to present in depth modeling of the protocol interactions, to propose cross layer routing metrics supported by a protocol selection and adaptation 3 mechanism, and to give an extensive experimental study where approaches in literature are compared. We believe that the outcome of this work could give important insights to influence the standardization process for the IoT.. The rest of the paper is organized as follows. In Section 2, we survey the related literature. Section 3, gives an overview of the basic functionalities/parameters of the IEEE 802.15.4 MAC and the IETF RPL protocols. In Section 4, we motivate our analysis and emphasize the importance of cross-layer interactions, considering the requirements of IoT applications. In Section 5, the interactions among MAC and routing are modeled. Then, Section 6 presents novel MACaware routing metrics, following the lines of our analysis. In Section 7, we present the experimental evaluation of the proposed metrics in comparison to existing approaches. Finally, Section 8 concludes the paper. In this paper, an analysis to characterize the complex inter-dependence among the basic MAC and routing protocols in IoT was presented. Moreover, a mathematical framework for joint optimization of the MAC and the routing layers parameters was proposed to enhance the existing standards. Specifically, novel metrics that take into account the dynamic behavior of the MAC and routing layers were introduced: R-metric and Q-metric. An extensive comparison of the existing ETX metric with the R-metric, which considers both the level of contention and the protocol parameters, was performed. It was shown that the R-metric achieves high average link reliability. However, it is not able to provide balanced reliability in the network. The Q-metric was proposed, having in mind that a minimum reliability or a maximum delay is required for all the nodes.. Extended experiments, where the proposed metrics were compared to the existing back-pressure routing, supported our mathematical analysis. The inclusion of such an analysis and an experimental study in the current standardization process could be very beneficial in the direction of improving the performance of. IoT protocols under realistic conditions. The importance of a mathematical modeling of MAC protocols related to sensor networking has been advocated in recent literature [8] - [10] . The critical effect of MAC parameters on the performance has been shown and discussed in [8] for IEEE 802.15.4 networks. In [9] , a Markov chain model is used to design a distributed adaptive algorithm for minimizing the power consumption of single hop star networks using the IEEE 802.15.4 MAC, while guaranteeing a given successful packet reception probability and delay constraints. In [10] , an automatic MAC protocol selection mechanism is proposed. The main idea of this approach is to provide a mathematical analysis of various MAC protocols, including the IEEE 802.15.4 MAC, and to choose the optimal MAC protocol and adapt its parameters for the selected modality, topology, and packet generation rate. In particular the designed mechanism takes into account the corresponding physical layer technology and hardware, while satisfying constraints for energy, 4 reliability, and delay. The value of the aforementioned approaches is that the algorithms do not require any modification of the IEEE 802.15.4 standard to be applied. However, their application is limited to single-hop networks.. Continuing our literature review in the MAC design and adaptation, a framework for MAC parameter adaptation based on analytical modeling has been also presented in [11] . This approach has been developed for XMAC and LPP protocol, but the results are not intended for the IEEE 802.15.4 MAC. An adaptation mechanism of the IEEE 802.15.4 MAC has been proposed in [12] . The mechanism was experimentally validated in a wireless body sensor network deployed for medical applications. However, this approach does not include any analytical modeling of the performance of the MAC and the adaptations are performed based on observations during their experimental study.. Within the literature related to the study of the routing protocols for IoT applications, [13] presents an experimental performance evaluation of RPL using the basic hop count routing metric and the expected transmissions count (ETX) [14] metric. The ETX is a reliability metric that indicates the number of retransmissions a node expects to execute to successfully deliver a packet to the destination node. However, the study in [13] does not consider the performance of RPL when a contention-based MAC protocol is active and there is no proposal about new routing metrics. In the back-pressure collection protocol (BCP) [15] , an extension of the ETX metric lead to the introduction of a dynamic back-pressure routing In [16] , the authors propose a metric for opportunistic routing for very low-5 duty cycled MACs that considers the expected number required to successfully deliver a packet from source to destination. In [17] , a multi-path opportunistic routing is proposed for time-constrained operations over IEEE 802.15.4 MAC.. However, load balancing and the effects of contention-based access are not considered in both previous approaches. A study on the interaction of RPL with the MAC layer is presented in [18] , where the use of a receiver-initiated MAC protocol in enhancing the performance of RPL is investigated. In [19] , a crosslayer framework has been proposed for IoT applications, by considering SMAC and RPL. However, these works do not take into account the specifications of the IEEE 802.15.4 MAC, which is widely used as the default MAC protocol for. IoT [3] .. Our work differentiates from the aforementioned approaches by modeling the IEEE 802.15.4 MAC and RPL protocol interactions, when applied in multi-hop networks. Considering complex multi-hop topologies, in comparison to single hop topologies of previous studies, we investigate the performance of the realistic network deployments for supporting IoT applications. In particular, we propose cross layer routing metrics supported by a protocol selection and adaptation mechanism. Finally, we present an extensive experimental validation of our approach in comparison to other routing metrics.

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


Universal codes of the natural numbers Abstract. A code of the natural numbers is a uniquely-decodable binary code of the natural numbers with non-decreasing codeword lengths, which satisfies Kraft's inequality tightly. We define a natural partial order on the set of codes, and show how to construct effectively a code better than a given sequence of codes, in a certain precise sense. As an application, we prove that the existence of a scale of codes (a well-ordered set of codes which contains a code better than any given code) is independent of ZFC. Lossless coding theory concerns the problem of encoding a sequence of symbols in some alphabet, usually binary. We demand two properties from our codes: they need to be (uniquely) decodable, and they should be concise, that is, the codewords should be short. In this paper we address the following problem: how concise can a coding system for the natural numbers be?. In 1975, Elias [Eli75] considered this problem and constructed a sequence of efficient codes, culminating in the so-called ω-code (almost the same code had been discovered by Levenshtein [Lev68] in 1968). The third member in Elias's sequence of codes, called the γ-code, is already asymptotically optimal in the sense that given a non-decreasing high-entropy distribution on the natural numbers, the expected codeword length is almost optimal; consult Elias [Eli75] for a formal definition.. Other concise codes have been constructed by Bentley and Yao [BY76] , Even and Rodeh [ER78] and Stout [Sto80] . These codes were analyzed by Ahlswede et al. [AHK97] . More recent examples are Yamamoto [Yam00] and Tarau [Tar10] . An online universal code has been constructed by Dodis et al. [DPT10] .. A natural question to ask is whether there exists an optimal code. We formulate this question in Section 3 and show that not only is there no single optimal code, but there is also no optimal sequence of codes. Since the proofs of these results are constructive, they can be used to construct a fast-growing hierarchy of codes. Elias's construction cannot be used to obtain this result, as we show in Section 3.1.. Care must be taken when considering the practical implications of these results: while all codes we consider are effective, they are not necessarily efficient, in the sense that encoding and decoding could be slow. Furthermore, in practice one is not interested in the asymptotic performance of a code, but in its performance on integers up to a certain application-specific bound, or even on a certain class of distributions.. We go on further and consider the existence of a scale of codes, which is an uncountable sequence of codes, ordered so that latter codes are better (in the sense of Definition 2.4 below), and containing a code better than any given code. We show in Section 4 that the existence of a scale is independent of ZFC, imitating classical results on functions on the natural numbers ordered by dominance. Fast-growing hierarchies. Theorem 3.1 can be used to construct a fast-growing hierarchy of effective codes. Let µ be a countable ordinal, and assign a computable fundamental sequence (α (i) ) i∈N to every limit ordinal α < µ. The fast-growing hierarchy (c α ) α<µ is defined according to the following rules. The base case is c 0 (n) = n + 1. For a successor ordinal α + 1, use Theorem 3.1 to construct a code c α+1 ≺ c α . For a limit ordinal α, use Theorem 3.1 to construct a code c α such that c α ≺ c α (i) for all i ∈ N.. Cardinal characteristics of the continuum. Section 4 shows that the existence of a scale of codes is independent of ZFC. However, a more satisfying answer will explain how this phenomenon is related to other cardinal characteristics of the continuum. Specifically, it is known that if we do not require our codes to be monotone, then the resulting poset of codes is Tukey-equivalent to the ideal of measure-zero sets [Bar10, Lemma 4.12]. Todorčević [Tod] conjectures that our poset is also Tukey-equivalent to the same ideal.

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


Simplified Relative Model to Measure Visual Fatigue in a Stereoscopy In this paper, we propose a quantitative metric of measuring the degree of the visual fatigue in a stereoscopy. To the best of our knowledge, this is the first simplified relative quantitative approach describing visual fatigue value of a stereoscopy. Our experimental result shows that the correlation index of more than 98% is obtained between our Simplified Relative Visual Fatigue (SRVF) model and Mean Opinion Score (MOS). When an observer watch a stereo image on the screen, the observer focuses to match the left and right images with converging eye movement, permitting each image to appear as stereoscopy by spatial perception of human nature. In this process, visual fatigue is occurred by various reasons; binocular disparity, motion parallax, texture gradients, occlusion, blur, etc. [1] . Resolving the problem of visual fatigue or discomfort after watching 3D displays, even for a short period, has been considerably researched in relation to stereoscopic displays [1, 2, 3] . Hoffman et al. [1] noted that conventional 3D displays create unnatural conflicts between accommodation and vergence. Lambooij et al. [2] also mentioned that the human eye experiences conflict between accommodation and vergence that mostly affects visual fatigue.. In this paper, we propose a so-called Simplified Relative Visual Fatigue (SRVF) model to evaluate the degree of the visual fatigue in a stereoscopy. Ukaia et al. [3] used a modified simulator sickness questionnaire (SSQ) evaluation commonly experienced by users of virtual reality systems. However, SSQ always need participants to obtain the result. Therefore, existing model evaluating visual fatigue is unsuited for measuring visual fatigue in run-time. Whereas, our model considers only the "accommodation and vergence" factors that can be calculated by disparities in a stereoscopy. Therefore, we suggest a new quantitative visual fatigue model, which can preprocess when capturing or displaying stereo images in real-time. In this paper, we demonstrated that our proposed SRVF model is sufficiently to be used to measure the quantitative visual fatigue value. The advantages of SRVF are 1) we can use in run-time to minimize visual fatigue 2) SRVF is of practical use due to monotonic formula 3) we can obtain optimal stereo images even though they are unfamiliar 3D contents. Accordingly, we suggest SRVF as a standard to measure quantitative visual fatigue.

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


Multiple solutions for a discrete boundary value problem involving the p-Laplacian Multiple solutions for a discrete boundary value problem involving the p-Laplacian are established. Our approach is based on critical point theory. The aim of this paper is to look for the existence of multiple solutions to the following problem. where, T is a fixed positive integer, [1, T ] is the discrete interval {1, . . . , T }, λ is a positive real parameter, ∆u(k) = u(k + 1) − u(k) is the forward difference operator, φ p (s) = |s| p−2 s, 1 < p < +∞ and f : [1, T ] × R → R is a continuous function. Our approach is based on the variational framework developed, in [1] , by R. P. Agarval, K. Perera and D. O'Regan to study problem (P 1 ); for p = 2, see also [2] . While the authors of the papers mentioned focus on the nonlinearity f , here we work with its primitive F(·, t) = t 0 f (·, ξ )dξ . More precisely, in order to establish the existence of at least three solutions to (P λ ) (Theorems 3.1 and 3.3), we point out a suitable relationship between the behavior of F with a precise bounded interval of parameters λ. The existence of nonnegative solutions (Theorem 3.2) is chiefly obtained by using a useful consequence of the strong comparison principle given in [1] (Lemma 2.1). However, we also obtain at least two positive solutions (Theorem 3.4) even if λ belongs to a precise half-line and f chances sign. To achieve our goal, we give a finite dimensional version of Theorem 2.1 of [3] , which is our main tool for investigating (P λ ) (Theorem 2.1). For more details on the subject treated here we refer the reader to [4, 5] and the references given therein. For completeness, we also mention closely related results given in [6] and in [4] (for p = 2) where the existence of at least one solution to (P λ ), for each λ lying in a suitable interval, has been proved by fixed point theory. Section 2 is devoted to auxiliary results and variational framework. The main results are contained in Section 3. Let c and d be two positive constants, we write. We now give the following theorem. . pΘ(c)(T +1) p−1 , problem (P λ ) admits at least three solutions. Proof. In applying Theorem 2.1, choice X = W , for every u ∈ W , put. and, for each λ > 0, J λ (u) := Φ(u) + λΨ (u). Clearly, J λ satisfies condition (Λ) and taking,. we claim that ϕ 1 (r ) < ϕ 2 (r ). To this end, we observe that for every u ∈ W , there exists j ∈ [1, T ] such that u( j) = max k∈[1,T ] |u(k)|. Therefore, taking in account that u(0) = u(T + 1) = 0, an easy computation ensures that. and by using the discrete Hölder inequality, one has. From this follows that. Further, since one has c < T +1 2. Hence, bearing in mind that Γ (d) > 0, by (b 1 ) and (2), we get. Combining (3) and (4), it is clear that the above claim is proved. Now, owing to (b 2 ) and again by (2), for every u ∈ W and λ > 0, we have. which clearly ensures that J λ turns out to be coercive. So, the assumptions of Theorem 2.1 are satisfied and our conclusion follows.. Remark 3.1. In many situations it is also important to obtain at least one solutions to (P λ ), see for instance Theorem 1.1 of [1] . In this order of ideas, a careful reading of the above proof reveals that condition (b 2 ) ensures the existence of at least one solution to (P λ ) for every λ > 0. Whereas, for c > 0, arguing again as above, but taking into account the proof of Theorem 2.1, we can show that, for every λ ∈ 0, By choosing for instance ρ = e 12 , c = 1, d = 12, s = 1 and p = 3, the assumptions of Theorem 3.2 are satisfied. Therefore, for each λ ∈ 164 10 6 , 344 10 6 , the problem. has at least three positive solutions.. Now we discuss the case s = p inside the growth condition (b 2 ).. Theorem 3.3. Assume that there exist three positive constants a, c, d with c < d such that (b 1 ) holds and in addition suppose that. , problem (P λ ) admits at least three solutions.. Proof. Arguing as in the proof of Theorem 3.1, it is clear that our conclusion follows again by Theorem 2.1 if we show that J λ turns out to be coercive. Indeed, keeping λ fixed as above, since for every u ∈ W , by (2), we get. Thus, begin λ <. , our claim holds and the proof is completed. . , +∞ , problem (P λ ) admits at least two positive solutions.. Proof. In applying Theorem 2.1, let J λ be as above and r =. which furnishes ϕ 1 (r ) = 0. On the other hand it is easy to see that by setting. So, since (b 5 ) implies f (k, 0) = 0, for every k ∈ [1, T ], Theorem 2.1 furnishes at least two nontrivial solutions, say u 1 and u 2 , to (P λ ). Moreover, a simple computation shows that u 1 and u 2 turn out to be also two solutions of the following problem −∆(φ p (∆u(k − 1))) = λ f (k, u(k)), k ∈ [1, T ], u(0) = u(T + 1) = 0, where, f : [1, T ] × R → R is defined by putting f (k, t) = f (k, t) if t ≥ 0; 0 otherwise.. We claim that u 1 and u 2 turn out to be positive. By contradiction, suppose that at least one, said u 1 , is non-positive. Thus, we have that u 1 fulfills the following conditions −∆(φ p (∆u 1 (k − 1))) = 0, k ∈ [1, T ], u 1 (0) = u 1 (T + 1) = 0.. On the other hand, bearing in mind that u 1 is nontrivial and also Lemma 2.1, one has that u 1 is positive, which clearly is a contradiction.

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


SIMILARITY AND PROTOTYPE-BASED APPROACH FOR CLASSIFICATION OF MICROCALCIFICATIONS Our aim is to show the utility of a format framework of measures of comparison, especially for a similarity based classification. We present both theoretical and practical arguments and we apply this approach to a real world problem.

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