Jong Gun Lee
email: jonggun.lee@lip6.fr

Sue Moon
email: sbmoon@kaist.edu

Modeling and Predicting the Popularity of Online Contents with Cox Proportional Hazard Regression Model

Keywords: Popularity of online contents, survival analysis, Cox proportional hazard regression model

Modeling and Predicting the Popularity of Online Contents with Cox Proportional Hazard Regression Model

Introduction

The emergence of Web 2.0 and Online Social Networking (OSN) services, such as Digg2 , YouTube3 , Facebook4 , and Twitter 5 , has changed how users generate and consume online contents. The YouTube site report of 24 hours worth of video upload every minute 6 witnesses that the amount of usergenerated contents is growing fast. Sharing and commenting on other users' contents via online social networking services constitute a significant part of today's Internet users' web experience. In this context understanding how do users find contents that are interesting? and how do certain contents rise in popularity? becomes of utmost importance. Such a mechanism will be extremely expedient in this age of information deluge both for providers and users that can privilege those mostly likely to get popular content.

The popularity of an online content is not a well-defined term. It is highly subjective and can be related to a mixture of endogenous and exogenous factors. The choice of factors varies among web services, persons and contents. Nevertheless, the popularity of online contents is by nature difficult to predict. For example, the flurry of contents and reactions happening very early in occasion of the death of Michael Jackson was probably far more than what could have been predicted by any model. Some contents become increasingly popular over time following a cascading effect [START_REF] Cha | A measurement-driven analysis of information propagation in the flickr social network[END_REF] and it is hard to predict which contents will eventually instigate such a cascading effect. It is also noteworthy that accessibility and observability of the predictor factors may vary; an OSN user has not the same visibility into OSN metrics than an OSN operator. The predictability of online contents will depend on which popularity metrics we have access to. It is therefore important to consider what factors do we take into consideration and which explicit data and related measures could be used to represent the popularity. Last but not least the popularity data are censored by nature; when one evaluates the popularity of an online content at time T , he can never be sure that this content will not continue to attract popularity in the future, so that the prediction done on a content popularity might be attained later. All these difficulties compound the challenge of predicting popularity.

In [START_REF] Szabo | Predicting the popularity of online content[END_REF], Szabo and Huberman presented a linear regression to predict the long time popularity of an online content from early measurement of user access pattern. More precisely, the authors of (2) observed a linear correlation between the logarithmically transformed long time popularity of a content with its the logarithm of its early measured popularity, i.e. they observe that the order of magnitude of the long time popularity can be predicted using the order of magnitude of early popularity. However, in order to obtain this linear correlation they had to remove from their analysis 11% of contents assumed as outliers. A more annoying fact is that because of the logarithmic transform applied to the popularity, the prediction errors behave as a multiplicative coefficient to the long-term popularity. This results in large prediction errors. The weakness of linear regression and of log-linear regression is also confirmed in [START_REF] Wu | On the use of reservoir computing in popularity prediction[END_REF], where a reservoir computation based on the prediction of the logarithm of the long-term popularity is proposed. However the reservoir computing predictor lacks of explanatory ability, so it is not easy to compare the effectiveness of different predictive factors on the long-term popularity.

In this paper, we take the standpoint of an individual user who has to infer the popularity of a content from publicly observable data, such as the lifetime of threads and the number of published comments. Based on OSN privacy rules, different users may have different views of popularities and their choices of measures would thus be different. Our goal in this paper is to propose a generic approach allowing users to weight the impact of different contributing factors and to choose the few most relevant. Our approach in this paper differs from (2) and (3); rather than targeting a precise prediction of the popularity that as explained will be anyway very difficult, we have an explanatory factor analysis, wish to determine, and weight explanatory factors that could explain the popularity of online contents. Our approach is related to survival analysis in biostatistics. A patient with a cancerous metastasis might stay alive much longer than predicted by his (her) doctors, when a healthy young person might die in a car accident. Nevertheless, predicting the likelihood that one will survive longer than a threshold or another individual is possible. In particular one can evaluate the effect of risk factors (like smoking, blood pressure, or more simply age) and compare their impact. We do not aim at inferring the precise popularity of a content but rather determine the likelihood or the probability that a content with given characteristics will attract popularity above a given threshold. We want also to be able to compare the explanatory power of different predictors in order to choose the most useful variables.

To address the above described goals, we will use in this paper a Cox proportional hazard regression model ( 4). This approach is frequently applied in biostatistics to model human survival and in reliability theory. This model works on the empirically observed Cumulative Distribution function (CDF) of the content popularity rather than working on the individual popularity values. It divides the CDF of the observable content popularity measure into two components: (a) one that can be explained by the given set of explanatory factors, called risk factors, and (b) a baseline distribution function that integrates all the effect and factors not taken into account by explanatory factors. We motivate later this modeling choice

We validate the use of this model over two datasets crawled from two online thread-based discussion forums: dpreview.com and myspace.com. Our datasets contain information about 267,000 threads and 2.5 million comments. The use of thread-based OSN is a particularity of this paper that differentiate it further from other works in the literature like (2; 3). Indeed, defining the popularity of a thread is more difficult as generally discussion forums do not provide any information about the content access statistics. We therefore assume that the popularity of a thread is captured by the number of comments in it and by its lifetime.

The contributions of our paper are:

1. We show that the survival analysis is applicable to model predict the popularity of an online content. For this purpose we have implemented the Cox proportional hazard regression model to predict the likelihood of a popularity metric using a set of publicly observable explanatory variables.

The survival analysis extracts from the popularity obser-

vation what can be related to the given explanatory factors from what cannot be related to them and should be assumed as coming from other sources not considered (or not observable) in the given explanatory factors (the baseline hazard). In particular we show that the baseline hazard can be modeled very well using a Weibull distribution, providing therefore a complete parametric model for describing the popularity of the online contents. Moreover the analysis enables us to weight the different possible explanatory factors and to choose a subset of them for modeling and predicting the popularity. 3. We validate our approach by modeling two kinds of popularity metrics, the lifetime of threads and the number of comments, with two different online discussion forums and show that our proposed approach is able to predict the likelihood of the fate of an online content after only a short period of observation.

The remainder of this paper is structured as follows. In Section 2 we explain survival analysis and the Cox proportional hazard regression model and in Section 3 and 4 we describe our prediction methodology. Section 5 gives our experiment results of predicting the lifetime of threads and the number of comments of threads. We present related work in Section 6 and finally we conclude this paper in Section 7.

Background

In this section we briefly explain the survival analysis and its three key functions in Section 2.1 and describe the Cox proportional hazard regression model and its interpretation in Section 2.2. Then we present how to interpret a set of distributions from two components of the Cox model in a figure in Section 2.3 and we explain why we use survival analysis and Cox proportional hazard regression model to model and predict the popularity of online contents in Section 2.4.

Survival Analysis

Survival analysis is a branch of statistics that deals with survival time until an event of failure or death. It is widely used in various areas, such as biology, engineering, economics, and sociology. However the approach is generic and can be applied to any random variable. We define three main functions, failure, survival, and hazard functions relevant to survival analysis. As we are applying survival analysis to another domain where the concept of death or failure is irrelevant, we need to define our terms. In the forthcoming and throughout this paper, T represents a random variable denoting the time until an event happens (for example an online content receive is last attention by its audience). We will name this event a "death" or a "failure" event; the value t the wall clock time.

Failure function F(t) and Survival function S(t)

The failure function F(t) or Cumulative Distribution Function (CDF) of the random variable T , is the probability to fail or die before a certain time t. It is defined as F(t) = Pr {T ≤ t}. We moreover define the Probability Distribution Function (PDF), f (t), as the derivate of F(t), f (t) = ∂F(t) ∂t . These definitions can be extended to the discrete case, where rather than time to die, we are interest in the number of events before dying. When the random variable K represents the number of events, F(k) represents the probability that the instance fails before observing k events. So the PDF, f (k), will be defined as:

       f (0) = F(0) f (k) = F(k) -F(k -1), ∀k ≥ 1 (1)
The survival function S (t) is the probability of survival up to a certain time t or the Complementary Cumulative Distribution Function (CCDF) of T ,

S (t) = 1 -F(t) = Pr {T > t} (2)
The survival function has the following remarkable property:

∞ 0 S (t)dt = E{T } (3)
In other words, the surface under the survival function curve gives the mean lifetime.

Hazard function -h(t)

The hazard function h(t) gives the failure rate at time t conditioned on the instance being still alive at time t, i.e., the expected number of failures happening at or close to time t. The hazard function is given by Equation (5):

h(t) = lim ∆t→0 Pr {t ≤ T < t + ∆t | T ≥ t)} ∆t (4) = f (t) S (t) = - S (t) S (t) (5)
One can define the cumulative hazard, denoted as H(t), as the overall number of failures that are expected to happen up to time t. The cumulative hazard H(t) is related to the survival function through the below relation:

H(t) = t 0 h(u) du = -logS (t) (6)
This results in this essential relationship between the cumulative hazard function and its survival function:

S (t) = e -H(t) (7)
Additionally, for the discrete case, h(t) is replaced by h(k) defined as :

h(k) = f (k) S (k) = 1 - S (k -1) S (k) (8)

Example: the Weibull distribution

We illustrate these three functions with a Weibull distribution (5) in Figure 1. A random variable T following a Weibull distribution with shape factor γ, and scale factor λ will have

f (t) = γ λ t λ γ-1 exp - t λ γ , t > 0, γ, λ > 0 (9)
F(t) = 1 -exp - t λ γ ( 10
)
E{T } = λΓ(1 + 1 γ ) ( 11
)
Var{T } = λ 2 Γ(1 + 2 γ ) -Γ(1 + 1 γ ) (12)
h(t) = γ λ t λ γ-1 (13)
H(t) = t λ γ (14)
where Γ(.) is the gamma function that extends the factorial function to non-integers. What makes the Weibull distribution remarkable and therefore widely used in survival analysis is the simple polynomial behavior of the cummulative hazard H(t) and the hazard rate h(t). This means that the death rate of a set of Weibull distributed random variables can be only related to time and will depends on only two factors α and λ. In particular, when γ = 1, f (t) becomes an exponential distribution, the hazard rate become constant, h(t) = 1 λ and the cumulative hazard grows linearly, H(t) = t λ . Under this condition, the constant hazard 3 rate means the remaining lifetime of an alive individual for a duration L does not depends on its age. When γ > 1, the dying rate is increased with time, i.e. the longer the variable lives, the more likely it will die soon. In other words, for γ > 1, the variable dies from oldness. When γ < 1, the dying rate is decreased with time, i.e. the longer the variable lives, the more likely it will continue to live.

Cox Proportional Hazard Regression Model

Cox proportional hazard regression ( 4) is a semi-parametric approach widely used for the survival analysis in practice. In the forthcoming, we describe the Cox regression model given that the failure time is a continuous random variable but the model can be extended in a straightforward way to the case where the failure time is a discrete variable K.

The Cox proportional hazard model assumes that the hazard rate function can be represented as a parametric linear combination of a set of risk factors

h(t) = h 0 (t) × exp(β 1 x 1 + β 2 x 2 + • • • + β k x k ). ( 15
)
The hazard function h(t) is composed of two components:

1. A parametric part that depends linearly on the risk factors. The risk factors (RFs) X = {x 1 , ..., x k } are the set of factors that influence the survival duration. As the risk factors are introduced through an exponential function, their effects become proportional, i.e., adding to the risk factor has a multiplicative effect on the hazard function. Therefore, the coefficient β i represents the relative importance of risk factors. 2. The non-parametric part defined as baseline hazard h 0 (t)

gives the natural risk. This function gives the hazard when any risk factor is not presented. No assumption is made about the form of h 0 (t) and its relation with time. Using Eq. 6, one can define a baseline survival function as S 0 (t) = exp(-t 0 h 0 (t)dt). The baseline survival S 0 (t) defines the CCDF describing the death time of a random variable that has not any risk factor in the set X.

The quantity β 1 x 1 +β 2 x 2 +• • •+β k x k , called
the prognostic value, is not dependent on time. It is used for evaluating the increase (or decrease) in risk with respect to the baseline hazard that an individual under study has to die.

Interpretation of fitting results

We illustrate the interpretation of the risk factor in Cox proportional hazard regression with three examples in Figure 2. We assume that S (t), represented by the dotted line, is an empirically observed distribution and S X (t) is the residual baseline survival function after removing the risk factors X = {A, B, C} by fitting the Cox proportional hazard model, i.e. S (t) is the survival distribution when the risk factors in X are present and S X (t) is the baseline hazard without presence of the risk factors in X. To simplify the discussion, we have presented the survival functions as straight lines. By definition S (0) = S X (0) = 1 and they diverge after. The wider the distance between S (t) and S X (t) is, the more effective the risk factors in X are.

1. Figure 2(a) shows that the overall lifetime is increased by the effects of the risk factor A as the expected lifetime in presence of A, S (t), is larger than in absence of A, S Ā(t) 2. Figure 2(b) shows that while lifetime is increased by risk factor A, it is decreased by B . 3. Figure 2(c) shows the empirically observed lifetime distribution S (t) and two baseline survival functions, S Ā(t) and S C (t). We say that the risk factor A is more a significant factor that C because the lifetime in its absence is shorter than the lifetime in abscence of C

Another interesting interpretation is related to Equation 3 and gives a more refined analysis. The surface under the curve of S (t) is the overall mean lifetime, while the surface under the curve of S Ā(t) is the mean lifetime after removing the impact of the risk factor A. We call it the mean baseline lifetime. By observing the difference between these two mean lifetime, one can evaluate the impact of the risk factor on the lifetime; the larger this difference is, the more expressive this risk factor is. This property will be used to propose a heuristic for choosing a subset of explanatory variable for predicting the popularity of online contents.

Discussion

In this subsection, we will compare Cox proportional hazard regression with other regression techniques and describe why we believe it is well suited to predicting online contents popularity. A review of the existing literature in popularity prediction shows that because of high randomness in empirical data predicting precisely the popularity of online contents has appeared as a difficult problem. One can explain it by the intrinsic unpredictability and sometimes unrationality of popularity. To get around this problem, (2) has applied a logarithmic transformation on popularity, i.e. they predicts the logarithm of popularity at the target time with the logarithm of the early popularity. Even after this transform the linear alignment is still not satisfactory and they have to remove 11% of their data (using a two class clustering) as outlier to be able to do their prediction. The authors of (2) do not provide any method for deciding a priori if a content will belong to the outlier class or not. Nonetheless, the result model leads to large error on predicting the individual popularity of content as the regression error are magnified by the exponential applied to inverse the effects of the initial logarithm transform, and act as multiplicative coefficients. We show in Figure 2.4 the cloud of points representing the thread lifetime versus the number of comments in the first 6 days in D-myspace. As shown, no linear alignment can be seen showing that the simple linear regression is not enough directly to predict the thread lifetime and we have to use another approach.

Whenever, the goals of (2) are similar to ours, the Cox regression is better suited to the goal. With the Cox Regression, we do not try to predict the popularity directly but rather the likelihood that the popularity cross a threshold, or in other term the probability that a content will survive longer (or attract larger number views or comment) in popularity competition. With this assumption we do not have anymore to remove outliers from our dataset. In fact the outlier effects are described by the baseline hazard that will capture the effect of all factors not taken into account in the risk factors. Whenever the baseline hazard can be modeled by a Weilbull distribution, one can state that baseline hazard is capturing the curse of time (or momentum) effect leading to a very elegant model where the risk factors capture the rational and explanatory factors of popularity and the Weibull baseline hazard that in fact accounts for modeling errors, only depends on time.

In [START_REF] Wu | On the use of reservoir computing in popularity prediction[END_REF], the authors used a reservoir computing neural network approach that achieve reasonable prediction results but their approach consists of predicting the popularity of an online content using a sequence of popularity observed for previous days. This goal is similar to the goal of authors of [START_REF] Gursun | Describing and forecasting video access patterns[END_REF] who apply an ARMA model to predict the popularity of a YouTube content based on the time sequence of past days popularity. Whenever these approaches are interesting they diverge from our goal that is to do a very early prediction of the fate of a content eventually based on a very limited number of observations. Moreover, the authors of (3) state in their conclusions that "The Reservoir Computing shows excellent prediction performance (...). However, as it is a black box, its prediction capability is difficult to analytically interpret; moreover, because it is usually required to predict the popularity in an early age, the randomization effect still exists in the prediction." A strong point of the Cox regression is the fact that the resulting model is very easy to analytically interpret in particular the value e β i , representing the relative importance of the risk factor i, which can be difficult to extract from a neural network approach. There is moreover a rich literature on interpreting the statistical significance of each coefficient β i of the Cox regression in a statistical test theory framework.

The authors of (7) compared decision tree, neural networks, and Support Vector Machines (SVMs) to determine if a content in the Digg cooperative recommendation and filtering OSN will attract a Digg score higher than a target. The goals of this prediction study are similar to our goals. While the prediction results show a good ability to predict the popularity, the approach is missing an interpretative framework that we have with the Cox proportional hazard regression, in particular the ability to compare the effectiveness of different predictive factor.

The above arguments and observation lead us to use the Cox Proportional hazard regression model. However, this model has also some weaknesses which are related to the argument on proportionality; risk factors have a multiplicative effect that is constant throughout the life of the individuals under study. To leverage this condition, authors in the biostatistics literature have suggested to use stratification or time products covariates [START_REF] Klein | Survival Analysis[END_REF]. However we do not need to do this in our case as the fitting results were quite good as the will be described in the forthcoming.

Selecting significant risk factors

Similarly to (2), we use as explanatory or risk factors, early values of the online content attributes that are visible to an external user, e.g. the number of comments or the number of hits after a short time of the creation of online content. Indeed, this choice is governed by the availability of only these metrics. An OSN operator could have access to richer variable and can use them.

However, the risk factors might be strongly correlated introducing collinearity that is harmful to the quality of inference. We have therefore to select and significant risk factors, instead of using them all. The problem of variable selection in models is a very classical problem. Several generic approaches have been coined to deal with variable choosing. Most of these techniques use penalized likelihood methods that penalize the likelihood with the number of variables used for constructing the model. For example, the LASSO technique uses penalized likelihood method with the L1-penalty [START_REF] Friedman | Additive logistic regression: A statistical view of boosting[END_REF]. The authors of (10) present a non-concave penalized likelihood approach adapted to the Cox proportional hazards model and compare it with the LASSO technique. They show that with a proper choice of the regularization parameter and the penalty function, their proposed estimators are able to choose correctly the best subset of risk factors for regression.

In our case we have only a small number of potential risk factor candidates, so in place of hammering the problem using a penalized likelihood approach, we will describe a visual heuristic based on the description given in section 2.3. This heuristic will also give more insight into the impact of each chosen risk factors.

Our heuristic consists of choosing among the potential risk factors the subset which maximize the performance of the predictive model. This involves a) making all possible combinations of the potential risk factors, b) applying the Cox regression model to each combination, and c) following what described in section 2.3, we calculate the mean baseline lifetime for each combination and evaluate its effect on the mean lifetime. We will choose the subset of potential risk factors that minimize the mean baseline lifetime. We have also to ensure that the potential risk factors are not too correlated to avoid multicollinearity. We ensure this by checking the correlation between every two candidate factors in order to avoid the simultaneous use of highly correlated factors.

Cox proportional model fitting

Formally, we model the hazard function h(t) of a popularity metric, through the Cox proportional hazard regression, which we described in Section 2. The popularity metric can be any measure of the popularity of online contents, such as the lifetime of threads and the number of received comments per thread and the risk factors can be any information related to the online contents and observed by a user who wishes to do the prediction of popularity.

After choosing the set of risk factors to be used, one can apply the Cox proportional regression to it and fit the long-term empirical distribution of long-term popularities obtained after the latest activity, such as statistics of comment and view of a content. This fitting can be done using one of the several libraries that implement it.

However, our goal is to apply the Cox proportional regression in order to predict the popularity of an online content as early as possible. We therefore fit different regressions generated using different values of initial observation period. This enables us to find an observation window where the information from risk factors are enough to obtain a good prediction of the likelihood of popular contents. We use the same heuristic as described in section 2.3; the further the baseline hazard goes from the empirical distribution the better becomes the predictive power of the variables relative to this observation period. As explained in section 2, a good fit of the baseline survival function with a Weibull distribution would be of high interest as it will provide an elegant parametric model for the empirical distribution. Thus as the last step we will fit the baseline survival function resulting from the Cox proportional hazard regression, to a Weibull distribution and evaluate the scale and shape parameters, γ and λ. Finally the total hazard function of an empirical observation is inferred as:

h(t) = γ λ t λ γ-1 exp(β 1 x 1 + β 2 x 2 + • • • + β k x k ) (16)
and using Equation ( 6) one can derive an approximation of the empirical survival distribution. This is noteworthy that the only source of error in this approximation is relative to the fitting of h 0 (t) with the Weibull distribution. The prediction therefore proceed by calculating for a given online content its risk factors, deriving its hazard function that gives the likelihood (through Equation ( 6)) that the online content will survive longer than a time t.

Experimental validation

In this section, we validate our proposed modeling methodology with two datasets crawled from two online discussion forums.

Datasets description

We collected two datasets, D-dpreview and D-myspace, extracted from online discussion forum services of forums.dpreview.com and forum.myspace.com. The Table 1 shows a brief description and gives some statistics about the two datasets.

The web service Dpreview provides its users discussion forums about the specifications of all kinds of digital cameras. The D-dpreview dataset was made by crawling the information of all threads and all comments related to the Canon EOS 40D-10D. This topic has the largest number of threads when we started our data collection. We collected for each post, either a thread or a comment, it position in the thread hierarchy, its anonymized user identifier and its posting timestamp. As the table shows, we have gathered the information of all posts posted during the five year between 2003 and 2007.

The OSN Myspace is a representative service where users create and share various contents, such as texts, images, and videos, with their social contacts. One of its functionalities is discussion forums where the users discuss and debate different issues categorized both by the service provide and the users. We crawled the information of all threads and comments from the Music-General forum, which had the largest number of threads among all forums, when we initiated our collection. We crawled from the forum the timestamp of each post posted between Jan 2004 to Apr 2008 and its position in the thread hierarchy. Differently from the D-dpreview, we could not collect the anonymized user identifier because the information was hidden by the service provider.

We plan to model describe the popularity of a thread using its lifetime and the number of comments in it. However we need to first define the lifetime of a thread. In the rest of this paper, we define the lifetime of a thread as the time span between the posting timestamp of the thread and the post timestamp of its last comment. Indeed as we have not access to the access statistics of thread contents, a thread can still be accessed and used long after the last comment is posted to it. Moreover it is not possible to definitely decide, if a comment is the last one as new comments might be added after our data collection. This means that our data might be censored. Because of this, we assume that a thread is dead if it does not receive any new comment before its expiration time. We choose the thread expiration delay using the distribution of inter-arrival time of two consecutive comments in a thread. We show in Figure 4 the CDF of inter-arrival time in D-dpreview and D-myspace, respectively. The CCDF in Figure 4 shows that almost all the inter-comments time is less than 1 day and also shows that about 99.5% of inter-comments time are less than 5 days for D-dpreview and less than 2 days D-myspace. Based on this observation we set the thread expiration delay for D-dpreview as five days (resp. two days for D-myspace).

Modeling the Lifetime of Threads

In this section, we present the results of modeling the popularity using thread lifetime. As explained we should use explanatory (risk) factors that are visible to an external observer. We assume that a user leaves comments on a thread that he is interested about. Based on this assumption, we have considered two class of potential explanatory factors: global and temporal factors. We choose the following three potential explanatory factors all observable and measured at the observation time:

1.The total number of comments in a thread. 2.The number of comments posted by the author of the thread.

3. The number of unique users beside the author.

The temporal interest is captured in the inter-commenting times. An interesting thread can be expected to receive frequently comments. Thus, we consider also the following information as potential explanatory factors for the temporal user interests.

4. The time until the first comment 5. The median of inter-comments time 6. The mean of inter-comments time 7. The variance of inter-comments time As we stated before, we have first to filter out useless factors which do not capture effects relative to the thread lifetime. For this purpose, we apply the heuristic described in Section 3. We In order to go further in the filtering, we checked the correlation coefficient among variables presented in shows that the first explanatory factor is highly correlated with the fourth one. Thus, instead of using both factors, we use only one of them and now we use the first risk factor. Thereafter we have fitted the Cox regression for all possible combinations of the four remaining explanatory factors, 1, 3, 6, and 7 and derive the surface under the resulting baseline survival functions. The results are shown in Table 5, where it can be seen that among all combinations the best results is obtained with the combination of the four explanatory factors. This combination of explanatory factors can explain 0.92 days (63%) out of the mean thread lifetime of 1,48 days. However a more precise look at table 5 shows that almost the same mean is obtained using explanatory factors 1 (number of comments) and 6 (mean inter-comment time) alone. We therefore decide to use this combination as explanatory factors.

Combination of risk factors

Mean value [START_REF] Cha | A measurement-driven analysis of information propagation in the flickr social network[END_REF][START_REF] Wu | On the use of reservoir computing in popularity prediction[END_REF][START_REF] Gursun | Describing and forecasting video access patterns[END_REF][START_REF] Jamali | Digging digg: Comment mining, popularity prediction, and social network analysis[END_REF] 0.56 [START_REF] Cha | A measurement-driven analysis of information propagation in the flickr social network[END_REF][START_REF] Wu | On the use of reservoir computing in popularity prediction[END_REF][START_REF] Gursun | Describing and forecasting video access patterns[END_REF] 0.56 [START_REF] Cha | A measurement-driven analysis of information propagation in the flickr social network[END_REF][START_REF] Wu | On the use of reservoir computing in popularity prediction[END_REF][START_REF] Jamali | Digging digg: Comment mining, popularity prediction, and social network analysis[END_REF] 0.58 [START_REF] Wu | On the use of reservoir computing in popularity prediction[END_REF][START_REF] Gursun | Describing and forecasting video access patterns[END_REF][START_REF] Jamali | Digging digg: Comment mining, popularity prediction, and social network analysis[END_REF] 0.68 [START_REF] Cha | A measurement-driven analysis of information propagation in the flickr social network[END_REF][START_REF] Wu | On the use of reservoir computing in popularity prediction[END_REF] 0.92 [START_REF] Cha | A measurement-driven analysis of information propagation in the flickr social network[END_REF][START_REF] Gursun | Describing and forecasting video access patterns[END_REF] 0.57 [START_REF] Cha | A measurement-driven analysis of information propagation in the flickr social network[END_REF][START_REF] Jamali | Digging digg: Comment mining, popularity prediction, and social network analysis[END_REF] 0.59 [START_REF] Wu | On the use of reservoir computing in popularity prediction[END_REF][START_REF] Gursun | Describing and forecasting video access patterns[END_REF] 0.72 [START_REF] Wu | On the use of reservoir computing in popularity prediction[END_REF][START_REF] Jamali | Digging digg: Comment mining, popularity prediction, and social network analysis[END_REF] 0.69 [START_REF] Gursun | Describing and forecasting video access patterns[END_REF][START_REF] Jamali | Digging digg: Comment mining, popularity prediction, and social network analysis[END_REF] 0.85 Table 4: Mean value of the fitted Weibull distribution with each combination of risk factors in modeling the thread lifetime (D-dpreview)

In Figure 5(a) and 5(d), we plot the empirical survival function S (t) and the baseline survival S 0 (t) obtained from fitting the Cox regression with four (resp. three risk) factors to D-dpreview (resp. D-myspace). As described before, we fit the resulting baseline survival S 0 (t) with a Weibull distribution and we present the result of the fit in Fig. 5(b) and 5(e). These two figures show that the two baseline survival distributions are well fitted with the Weibull distribution. The obtained scale and shape parameters are γ = 0.9909 and λ = 0.4286 for D-dpreview (resp. γ = 0.8616 and λ = 0.101 for D-myspace). The shape factors γ for the two dataset are very close to 1, meaning that the baseline survival can be described as an exponential distribution and therefore a constant death rate of 2.33 (resp. 9.90). This is an important outcome as it means that only the risk factors to explain the variations in the lifetime of different threads as the age is irrelevant.

In Figure 5(c) and Figure 5(f), we investigate if we can model the empirical survival function using the explanatory factors but observed early in the lifetime of the thread. For this purpose we fit a Cox regression model but with explanatory factor observed at different time interval (1 day to 6 days for D-dpreview and 3 hours to 24 hours for D-myspace) after the creation of the thread. The resulting baseline hazard moves to the left with increasing observation time. Interestingly observing a thread only 6 days over D-dpreview, give as much information as observing its explanatory factor over the whole lifetime. Similarly for D-myspace, observing a thread 24 hours after its creation give almost as much information about the fate of the thread than observing it explanatory factor over the whole lifetime.

In order to show the predictive power of the explanatory factors, we plot in Figure 6, the thread lifetime as a function of the overall hazard e i β i x i . It can be seen clearly that a large hazard results in a small lifetime, and inversely all long living threads have small risk. Nonetheless, the relationship between overall risk and thread lifetime is not unique and therefore does not enable a precise prediction, but rather the prediction of the likelihood of the thread lifetime.

Modeling the Number of Comments in a thread

In this section we extend the analysis done in previous section to the modeling and prediction of the number of comments in a thread. We use the same D-dpreview dataset described before, with the same seven potential explanatory factors used in the previous section. However there is a slight issue there. Using the number of comments in a thread for predicting it seems trivial. In fact the practical prediction problem consists of using the early measurement of number of comments in a thread to predict its final value. We therefore will use the number of comments at the thread expiration delay as explanatory (risk) factor. This choice is motivated by the fact that at before thread expiration delay the number of comments is not yet definitive and predicting it is still meaningful.

We show in Table the surface under the baseline survival function of the number of comments in D-dpreview dataset. As the overall, mean number of comments is 10.47 clearly, variables 2,5,6 and 7, are not very expressive. Resulting in a choice of explanatory factors as 1,3 and 4. A check on their correlation shows that the correlation between these variables is not large. Applying the heuristic described in section 2.3, we checked all combination of these three explanatory factors. The results are shown in Table 6. This table shows that the variables 3 and 4 are the most effective in explaining the number of content as they can explain 3.84 comments among the mean of 10.47 comments per thread. The parameters of the calibrated Weibull Similarly to the thread lifetime modeling we show the effect of the observation time to predict the number of comments per thread. For this, we vary observation time as shown in Figure 8. This figure shows that when we use the information captured during the first 24 hours, the information for risk factors is not enough to predict the number of comments. The baseline survival function using the information observed for more than 2 days, however, is close to the baseline survival function based on the whole observation. Thus, we could closely predict the number of comments of threads after observing the information on risk factors for more than 2 days.

Finally we will illustrate here how we can use the model developed in this paper to predict if a thread will attain more than 100 comments. We found in D-dpreview dataset that there are 9(a) we plot the number of comments received after one, two, and three days. After one day (resp. two and three days) about 24% (resp. 56% and 73%) of the 1406 threads that have achieved more than 100 comments, will attain 100 comments. Now let's suppose that one observes a thread and calculate a prognostic value of the explicative factor. Now let's predict that the thread will attain the 100 comment if the prognostic value is less than a threshold L. We show in Figures 9(b) to 9(d) the performance of such predictor as a function of the value of the threshold when the prediction is made after 1,2 and 3 days. For example the first day, using a threshold of -200 for prognostic value enable to detect correctly 80% of threads attaining 100 comments. There will be some threads that have been misclassified (false positive threads). The mean number of comments in these thread is equal to 63. Indeed with larger observation time performance improves; the third day, the same threshold enable to detect more than 95% of such thread. authors of [START_REF] Crane | Robust dynamic classes revealed by measuring the response function of a social system[END_REF] analyzed the relaxation response after the bursty activities caused by endogenous and exogenous factors in Youtube videos. Among all burst activity, the authors that some are followed by a ubiquitous power-law relaxation governing the timing of views. They showed that these bursts could be explained by an epidemic cascade of actions.

Our work is different from the existing literature by our explanatory approach based on Cox proportional hazard model. Rather the predicting the popularity values directly we predict the likelihood that a content will become popular.

Conclusion

In this paper, we proposed a methodology for modeling and predicting the popularity of online contents. We applied Cox proportional hazard regression with a set of given explanatory factors to model and predict an objective metric of the popularity of online contents. We validated our approach by modeling and predicting two kinds of popularity metrics: the threads lifetime and the number of comments, with two datasets from two different online discussion forums, dpreview.com and myspace.com. In our experiments, we, showed that the proposed methodology could predict the lifetime of Dpreview (resp. Myspace) threads by observing a thread during the first 5∼6 days (resp. 24 hours). Moreover, our approach was able to predict the number of comments in Dpreview dataset, by only observing a thread during its first 2∼3 days. Finally, we presented how the approach can be applied to predict the set of threads which would receive more than 100 comments. This last example validates the flexibility of the proposed method.

Figure 1 :

 1 Figure 1: Examples of survival, failure, and hazard functions with a Weibull distribution

Figure 2 :

 2 Figure 2: Examples for understanding risk factors (RFs) and survival function

Figure 3 :

 3 Figure 3: Thread lifetime versus the number of comments in the first 6 days in D-myspace

Figure 4 :

 4 Figure 4: CCDF of Inter-comments time

Table 2 :

 2 Mean value of the baseline survival function of the thread lifetime in D-dpreview dataset. Each mean value is obtained after removing the effect of an individual risk factor. The overall mean thread lifetime is 1.48 present in table 5 the mean value of the baseline survival function of the thread lifetime derived as the surface under the baseline survival function after removing the effect of an individual risk factor. One can see that variables 2 and 5 are almost useless as these factors can only explain a value 0.1 day out of the overall delay 1.48 day. We therefore remove these two factors from potential explanatory factors and consider only the five remaining explanatory factors: 1. the number of comments, 3. the number of comments by a thread poster, 4. the number of comment contributors, 6. the mean of inter-comment time, 7. the variance of inter-comment time.

  Fitting it with a Weibull dist.

-

  Shape Parameter: 0.8616 (e) Fitting it with a Weibull dist.

10

 10

Figure 5 :

 5 Figure 5: Prediction of the lifetime of threads from D-dpreview (upper figures) and D-myspace (lower figures)

Figure 6 :

 6 Figure 6: risk factor component (x 1 β 1 ...x k β k ) vs. the lifetime of threads

Table 5 :

 5 Mean value of the baseline survival function of the number of comment in D-dpreview dataset. Each mean value is obtained after removing the effect of an individual risk factor. The overall mean thread lifetime is 10.47. The variable 1 is the number of comments at thread expiration delay distribution are γ = 1.83 and λ = 7.8. We show in Figure 7 the result of the fitting.

Figure 9 :Figure 7 :

 97 Figure 9: Predicting the threads to have more than 100 comments. (In (b), (c), (d), straight lines are true positive values and dotted lines are mean values of false negative values.)

Figure 8 :

 8 Figure 8: Determining a minimum observation time

Table 1 :

 1 Description of our two datasets crawled from two online discussion forums

	Dataset	Service address	Forum name		Start -End (Duration)
	D-dpreview	http://forum.dpreview.com	Canon 40D-10D		2003/01 ∼ 2007/12 (5 years)
	D-myspace	http://forums.myspace.com	Music -General	2004/01 ∼ 2008/04 (4 years and 4 months)
	Dataset	# threads	# comments	# unique users for threads	# unique users for comments
	D-dpreview	140,524	1,496,808		27,989	41,269
	D-myspace	127,607	1,038,989		-	-

Table 3 .

 3 The table

	RF	1	3	4	6	7
	1	1.0000	0.6429	0.9124	-0.0004 0.1000
	3	0.6429	1.0000 0.4777 0.1000	0.1000
	4	0.9124	0.4777 1.0000 0.0000	0.1000
	6	-0.0004 0.1000 0.0000 1.0000	0.8530
	7	0.1000	0.1000 0.1000 0.8530	1.0000

Table 3 :

 3 Correlation coefficient between two risk factors (RFs).Each number for RF is the same one used in above.

Table 6 :

 6 Mean value of the baseline survival function of the number of comment after removing combination of explanatory factors. The overall mean thread lifetime is 10.47.

	Combination of risk factors	Mean value
	(1, 3, 4)	6.61
	(1, 3)	7.21
	(1, 4)	7.21
	(3, 4)	6.60

http://www.digg.com

http://www.youtube.com

http://www.facebook.com

http://www.twitter.com

http://www.youtube.com/t/fact sheet (accessed onNov 1, 2010)

Related Work

In this section, we briefly describe the literatures related to our work.

• Survival analysis

Survival analysis [START_REF] Schlittgen | Survival analysis: State of the art[END_REF] has been applied to various areas, such as bio-medical science, sociology, and epidemics (12; 13; 14; 15). Among the methodologies for survival analysis, Cox proportional hazard regression model (4), which is a semi-parametric survival analysis methodology, has been widely used (16; 17; 18). In this paper, we applied the survival analysis and Cox proportional regression approach to model and predict the popularity of online contents.

• Analysis on Threads and Comments

The authors of (19; 20) analyzed the posts and comments of Slashdot. In detail, the authors of ( 19) explained the behaviors of inter-posts times with statistical models while the authors of (20) analyzed the dynamics of posts and users. ( 21) characterized the social interactions of Slashdot users by graph theoretic approach. In (22; 23) user comments information wer used to understand user intention. In [START_REF] Agarwal | Identifying the influential bloggers in a community[END_REF] the detection of influential authors based on user comments was investigated.

• Modeling Inter-Posting and Popularity prediction

The authors of ( 19) modeled comment time interval with four different statistical models and they predicted intermediate and long-term user activities.In (2) a popularity prediction methodology for online contents was proposed that was based on the correlation of popularity between early and later times. The authors of (25) proposed three prediction models and validated them over Youtube and Digg datasets. In [START_REF] Salman | Diggin digg: Comment mining, popularity prediction, and social network analysis[END_REF], a co-participation network among Digg users was proposed that was used to predict the popularity of online contents using an entropy measure. The


Kishiro Yukito

Denis Taillandier

Phd

Nanotechnology through the Lenses of Science Fiction Case Study of the Manga Ganmu (Battle Angel Alita) by

Introduction

Since Richard Feynman's famous speech "There's plenty of room at the bottom" and Taniguchi Norio's coining of the term in 1974, nanotechnology has emerged as a rapidly growing field whose dynamics and prospects pose many challenges to scientists and society at large. Most of the articles dealing with nanotechnology stress out the fascinating progress made by those who work at the nanoscale. Nanophilic perspectives range from the next industrial revolution to a quasi-religious vision of a "post human" future freed from any restriction. However, in the same speculative way, insofar as Drexler's "nano robots" are yet to appear and the industrial "nano revolution" has not happened, a nanophobic rhetoric has come to light, emphatically pointing to the potential harms of nanotechnology. It is therefore of interest to note that even though (or precisely because) nanotechnology is still in its infancy, its rhetoric revolves around two radically opposed views. As C. Milburn or J. lopez point out, such a hyperbolic way of presenting a future where the world is recreated atom-by-atom relates on the use of Science Fiction rhetoric, thus blurring the boundaries between "real" science and SF. Basing his reflection on Baudrillard's hyper reality concept, Milburn goes as far as to say that nanotechnology is a "science fiction" (112). Such an analysis, beyond its post-modern perspective, draws the attention to the close connections between the imaginary and Science. Science does shape our every-day life and influence our imaginary, but, and this seems of less concern, the opposite also holds: scientific activity and its technical corollary borrow much from the imaginary, which SF is a part of. SF has always explored the possible consequences of technological innovation, attempting to give meaning to the social transformation related to science and technology. Avoiding to fall into an apologia for scientific progress, it is a literary genre which presents an imaginary world where the use of fictitious technology urges the reader to question the principles upon which society order rests. Japan is a wonderful example inasmuch as manga and animation, as a modern medium for SF, have dealt a lot with the political, economical, social and ethical impact of nanotechnology. Among others, works like Ganmu (Battle Angel Alita) by Kishiro Yukito, or Ghost in the Shell by Shirō Masamune provide an interesting base to consider the way Japanese science fiction apprehends a nanotechnologicaly modified future. This is what I propose to further analyse in this modest article by focusing on the former only for the sake of time and space.

A Historical perspective

Before embarking on the analysis of how Kishiro depicts nanotechnological development and its interactions with society, it is necessary, first, to consider his work on a historical perspective. This will help us understand the scientific base upon which the mangaka built his reflection and allow a quick overview on nanotechnology itself: its definition (as reported by the main actors in the field: the US, Japan and Europe), a brief chronology of events and the most prominent features of the scientific debate about its development.

Ganmu was originally published in Shūeisha's monthly Business Jump Magazine from 1990 to 1996 and released in the form of 9 volumes (Tankōbon) at the same period. Kishiro later designed some other works based on a similar dystopic world 1 and eventually decided, in 2000, to carry on the Ganmu cycle with Ganmu: Last Order, in which he created an alternative ending to the first opus and resumed anew with a story whose conclusion he was not satisfied with 2 . However, and even though Ganmu: Last Order provides a new reading of nanotechnology social implications, as Bounthavy Suvilay points out in her very interesting article about nanotechnological representations in Manga, I will focus only on the first series, arguing that its ending (which Suvilay chose to overlook) unveils the metaphysical and epistemological foundation of nanotechnology in the 80's and 90's and offers an ethical conception of its development.

Nanotechnology, a synopsis

We need thus to sketch a succinct outline of what nanotechnology is. There is still some debate around the exact definition, but the American NNI 3 (National Nanotechnology Initiative), the European Commission 4 and the Japanese MEXT 5 share a similar interpretation. The prefix "nano" derives from the Greek word "nannos" meaning "dwarf" and is used in mathematics to signify one billionth of a meter (10 -9 ). Conceptually, nanotechnology refers to science and technology at dimensions between 1 and 100 nanometers and to the new properties that can be understood and mastered at that level. As Senior Advisor for the NNI Mihail C. Roco points out: "The nanoscale is not just another step toward miniaturization, but a qualitatively new scale with unique properties, phenomena and processes" (2003, 1). One of the examples usually given to illustrate this point is that of gold: whereas 1 ton, 1 kilogram or 1 gram of gold possess the same physical properties, it is not true when reduced to the nano scale. Its color, melting point and chemical characteristics change as a result of the quantum interactions that prevail in small structures: "Nano gold doesn't act like bulk gold" (1).

It is the famous physicist Richard P. Feynman who is generally credited with having symbolically given birth to nanotechnology in his speech at the American Physical Society annual conference in 1959, when he asserted: "The principles of physics, as far as I can see, do not speak against the possibility of maneuvering things atom by atom" (29). But it is later in the 80's that Eric K. Drexler, an engineer at the MIT, building upon Feynman's assumption that "we can make a thing very small which does what we want" (35), popularized the word of nanotechnology. In his book Engines of Creation, he theorized the notion of a nanoscale assembler, a tiny machine operating through a built-in program that would be able to build a copy of itself and of other items by manipulating atoms directly. As Drexler himself puts it:

The resulting nanotechnology can help life spread beyond Earth -a step without parallel since life spread beyond the seas. It can help mind emerge in machines -a step without parallel since mind emerged in primates. And it can let our minds renew and remake our bodies -a step without any parallel at all. (21) Drexler's vision of Nanotechnology, as "a very tall and slender tower of reasoning" 6 it may look, did spark a lot of attention and participated in laying the path to the launching in 2001 of the American National Nanotechnology Initiative (NNI) with a $ 465 million budget. A collaborative model largely followed by Japan and the European Union. Thus, even though Drexler's ideas have been later dismissed as being science fictional and not applicable 7 , the opponents to his molecular manufacture scenario do acknowledge his tremendous effect on the field of nanotechnology. Besides, critics of Drexler's nano-assemblers feasibility have more or less arisen quite late in the 90's (and more vehemently from 2000), and probably largely because of the issues about nanotechnology ethical and social implications raised by Engines of Creation.

The controversy around Drexler's nano assemblers does not actually call into question the control of nanophenomena, but rather rejects its mechanical conception (the very idea of engineering nano robots that would manipulate atoms according to a program), emphasizing instead a chemical approach. As B. Bensaude-Vincent has pointed out, the machine metaphor is "all-pervading" (2006, 9) and can also be found in biology and chemistry. However, as she skillfully illustrates, the bottom-up process (building up single components into more complex assemblies) advocated by chemists rests upon self-assembly: an organized structure is formed as a consequence of specific, local interactions among the components themselves, without external direction. Now, such a process implies that chemistry's use of the machine metaphor offers at the same time an "anti-mechanistic" view that "do not deprive matter of spontaneity, of dynamis" (2006, 21). This is of course an oversimplified account of the development of Nanotechnology, and I have skipped many of the interesting and intricate debates or issues associated with the multiplicity and variety of the scientific works in the field. But the scope of this article is not a scientific journey into the nano world, it rather attempts at probing the connections between nanotechnology and science fiction, so that a quick survey should prove sufficient to frame the analysis. The very fact that most of the social studies related to nanotechnology have arisen in the late 90's underlines the significance of works of science fiction that were published before, insofar as their author were precisely concerned with the same issues. It is therefore quite interesting to have a look at the way nanotechnology was depicted in a popular medium such as manga between the mid 80's (when Drexler published his book) and the mid 90's, at a time when it emerged as a prospectively revolutionary field. In this respect, Ganmu provides a compelling narrative that draws the reader into a technologically modified world, urging him to ponder over the interplay between society and science and the way it is modified by nano applications.

Kishiro's vision on nanotechnology, the "tech-notes"

When looking at the commentaries and technical notes inserted at the end of each volume, and in particular those of Volume 5, Kishiro's understanding of nanotechnology is clearly based upon Eric Drexler's Engines of Creation, published a few years earlier in 1986. Besides, it is worth noting that in the little chronology he gives of nanotechnology "genesis", he does not mention the fact that the very word of nanotechnology was coined by Japanese scientist Taniguchi Norio, professor of Tokyo Science University, in a paper published in 1974 (some 12 years before Engines of Creation). He does refer, as all chronological accounts on the origin of nanotechnology start with, to the seminal speech by Richard Feynman, but interestingly points to a narrative preceding it: the science fiction novel Waldo by Robert A. Heinlein. For Kishiro, nanotechnology "Ur-text" 8 is thus not a scientific work but a narrative fiction depicting a scientist who designed mechanical hands (the "waldoes") of decreasing size, controlled by an operator and enabling him to manipulate matter at a microscopic level. Some social scientists have already underlined the fact that Heinlein ideas were deeply interwoven in Feynman's speech and his vision of tiny tools, but, again, most of them published their paper after 2000 (Toumey, 2006 and Milburn, 2004). Kishiro had already worked out this connection quite early and it is therefore not surprising that his picturing of nanotechnology bears close relation to the futuristic writings of Drexler, notably his vision of nano assemblers (also refered as "nano machine", "nano robot" or "nanobot"). For Kishiro, the possibilities envisioned by Drexler provide a fertile ground to reflect upon a technology of "creation", "abundance", "healing" and… "destruction" (as Engines of Creation's chapter title shows).

However futuristic Drexler's molecular engineering may sound, the invention in 1981 of the Scanning Tunneling Microscope (STM), enabling the imaging of atomic surfaces by using a tip which scans the electric field at the surface of the sample, strengthened its stance. As luck would have it, it was later discovered that the STM made the individual manipulation of atoms and molecules possible. This led to the drawing of the IBM logo with single atoms of Xenon in 1990, the latest "achievement" Kishiro refers to in his chronology but not the least because it added plausibility to Drexler's theory and happened exactly when Kishiro started Ganmu.

Thus, Kishiro's drawing of "mad-scientist" Nova's nano assemblers reflects Drexler's design, with tiny dots reminding the imaged picture of the IBM logo and a round "body" resembling the fullerene (a spherical, entirely carbon-based molecule discovered in 1985). Fitting Drexler's description, Nova's assembler is a "molecular-sized robot, with gears, bearings and motors" 9 , it has tiny arms 10 and can self-replicate (here, Kishiro's note literally quotes a part of chapter 2 from Engines of Creation 11 ).

As far as the possible applications of such assemblers are concerned, Kishiro is first concerned with pushing back the frontiers of physical and biological determinism. Drawing from Drexler's insight that "by restoring all the cells and tissues of the body to a youthful structure, repair machines will restore youthful health" 12 , he alludes to brain restoration and eventually to immortality (as long as there's no total dissolution of the body and the brain). From such a postulate of conceivable "restoration", there's only a nano-step to "enhancement" which Kishiro makes promptly: brain enhancement, artificial spinal column, mechanical prosthesis, and so on. As Norbert Wiener has pointed out, after having modified their own environment to better suit their condition, humans have now turned to their own nature, modifying it for the sake of their own projects 13 . The "anthropotechnic" (Goffette, 2006) process of modeling and improving the human body had begun long before with the coining of what has become a powerful trope in the 20 th century, namely the cyborg 14 . But what has nanotechnology to do with the cyborg imagery originally associated with cybernetics and the theory of information?

It is largely because the latter have made the same attempt at reducing everything to information (the theory of communication and control applies equally to humans, animals and machines, hence the informational pathways connecting organic and synthetic components) as the former is trying to reduce everything to atoms (the fundamental bricks of matter, may it be organic or not). What's more, promoters of nanotechnology, especially the American NNI, are advocating what they call the NBIC convergence which aims at unifying Nanotechnology with Biotechnology, Information Technology and Cognitive Sciences. As W.H. Wallace puts it:

If the Cognitive Scientists can think it the Nano people can build it the Bio people can implement it, and the IT people can monitor and control it (Roco and Bainbridge, 2003, 13) The unifying process sparked by nanotechnology supporters, by trimming down everything (information, matter, life…) to what critics like the ETC Group have nicknamed the "little BANG" (Bits of information, Atoms, Neurons and Genes), contributes to the blurring of boundaries between human and machine, matter and information, nature and artefact, organic and inert matter. But then, how does Ganmu tackle this new perspective? Katherine Hyles convincingly makes the point that "the literary texts often reveal, as scientific work cannot, the complex cultural, social and representational issues tied up with conceptual shifts and technological innovations" (24), so that looking into a cyber-punk-labeled manga such as Ganmu should provide some insight into how the characters make sense of those blurring boundaries.

Shifting boundaries, Ganmu and the enabling technologies

The storyline -technological achievements

The manga takes place in a seemingly dystopic world where the reader is first brought to Kuzutetsu, literally the "scrap yard", an urban sprawl that looks like a huge slum, host to a bunch of technologically enhanced cyborgs mingling with common humans. It is spreading under the celestial city Zalem, with which it was once linked through an orbital skyhook 15 made out of carbon nanotubes. The connection was however severed and Kuzutetsu became Zalem's dumping ground while remaining under its rule: its factories supply water, food and basic resources to the floating city via the linear tubes, their very last and unrequited relationship. The factories act also as a local government, enforcing Zalem regulation through the "deckmen": a type of cyborg with a cylindrical body allowing them to move within the linear tubes. They were former humans who relinquished a part of their brain (along with their memories, their will and desires) so that the remaining chunk works more or less as a computational chip processing the information coming from Zalem 16 . The deckmen are for instance responsible for the recruiting of bounty hunters (referred to as "hunter warriors", the only "police" of Kuzutetsu), whose glial cells 17 they imprint with an identity code upon their registration. Such an identifying measure clearly reminds of the miniaturization of existing RFIDs 18 and their implantation in the human body for monitoring purpose (Laurent and Petit, 260-3). Kuzutetsu appears as an ultra-liberal world going hand in hand with a frantic technological development where a rough type of cyber technology prevails which enables the creation of hybrids out of the mastery of inert matter and microscopic engineering. Kuzutetsu's cyber technology cannot however do without the actual biological brain which is deemed to be irreplaceable. Whereas the body is interchangeable at will (insofar as one can afford it), death of the brain signifies the actual and irreversible death of the subject. There remain also "genuine" 19 humans who either don't have the financial means to undergo a procedure of body enhancement or utterly refuse the replacement of their biological body. Now, it is in the midst of such a composite and wild environment that a former citizen of Zalem discovers the head and the upper torso of a cyborg whose brain is still alive. He succeeds in saving her, provides her with new bodies and a name: Gally. But she has lost her memory and can only rely on her fighting technique to catch a glimpse of her past and of herself. The story therefore unfolds as a picaresque story about the initiatory journey of Gally who is the narrative point of the manga: it is through her eyes that the reader discovers Kuzutetsu, its surroundings and ultimately Zalem. But the ongoing disclosure is not merely geographical, it also encompasses, at every stage of the story, new technological achievements and the meaning they take through their implementation within society. One of the most central characters in Gally's peregrination is "mad-scientist" Desty Nova, a citizen of Zalem excluded from the floating city because of his immoral and hazardous attempt at transcending human determinism through various in vivo experiments. It is Nova who is behind many of the obstacles and challenges Gally has to overcome, though their first encounter takes place only on the 5 th volume of the series. Following Lévi-Strauss' work on the structure of Myth, Nova's character can be interpreted as a trickster that establishes some relation between the two antagonistic spaces, mediating between Zalem and Kuzutetsu, but creating at the same time confusion and disorder 20 . As we will see later in the last section, the mediation figure is then overridden by Gally who finally succeeds in bringing together the opposites into what Eliade (1989) calls the "conjunction oppositorum", thanks however to Nova's nanotechnology. Indeed, for now suffice is to say that nanotechnology appears in Kuzutetsu through the mediation of Nova who achieved the creation of the nano restorers Drexler was envisioning. The technological accomplishments of Zalem are thus far more advanced that the cyber technology prevailing in Kuzutetsu. As long as the brain is not entirely destroyed, nanotechnology can restore or reconstruct it as well as heal any corporal wounds. However, Nova uses nanotechnology to "play" with human destiny in order to overcome a determinism he associates with the second law of thermodynamics 21 and the inescapable death of the world by increasing entropy. His struggle is therefore nothing but an attempt at overcoming death and the restrictions imposed by the inexorable decay of matter. Nanotechnology becomes, as Bensaude-Vincent has put it, a mean to "free the human from material determinism" (2009, 104), to constantly push back the limits instead of negotiating with them. The flesh-and-bones humans of Zalem, whose "humanhood" lies precisely in their biological constitution, have access to a more powerful, intimate and subtle technology. They look down on the mechanical hybrids that populate Kuzutetsu and boast about their own superiority that enable them not so much to work on matter (fabrication, engineering) but to produce it de novo by reorganizing and reassembling its fundamental bricks. But Nova unveils a secret they don't even know of: the initiation they undergo at 19 to become full citizen of Zalem is a surgery whereby their brain is replaced by a bio-chip containing a copy of their memory. Besides, this process is part of a eugenics program implemented by an Artificial Intelligence that controls the whole floating city: the computer Melchizedek [START_REF]the King of Salem (an alternative reading of Jerusalem)[END_REF] . Its purpose was to construct a "perfect" society by normalizing its members through a protected and "stressless" but complete management. The ultimate goal was to send them to other planets and establish new colonies outside the solar system, a long-term journey which cannot afford any conflicting human relationships if it is to succeed. But the project failed and the AI computer went mad as it lost its "beloved children". It severed the skyhook link from the orbital station Jeru (the counterweight of the space elevator) and from Earth surface, secluding Zalem from any external influence.

Dematerialization of the body and the mind? What stands as human within the evolving natural/artefactual and body/mind polarities?

The progressive disclosure of technical achievements in Ganmu gradually calls into question the modern epistemological divide between the natural (as a safe and unchanging ontological ground) and the artefactual (as a man-made counterfeit of nature). Elaborating first from cybernetics, Kishiro illustrates the construction of the cyborg which, as K. Hyles points out: "presumes a conception of information as a (disembodied) entity that can flow between carbon-based organic components and silicon-based electronic components" (2) so that the materiality of the body is just an epiphenomenon of the informational pattern. Interestingly, Kishiro depicts two kinds of cyborg: a biological brain coupled with a mechanical body (in Kuzutetsu) and a bio-chip coupled with an organic body (in Zalem). The latter is however inspired by nanotechnology, and relies on the use of a more subtle and intricate technological trick echoing the transhumanists' hope of downloading the human brain into a computer (Moravec, 1990). But then, where does the essence of "humanhood" lies if a bio-chip can act as a human brain and mechanical prosthesis replace the body, if an AI computer can display the same behavior as a human being?

To deal with this question, the manga is based on an apparently unique dichotomy: that of organic life as opposed to machine [START_REF] Nova | unveiling the secret of Zalem, ponders over the difference between a cyborg with a biological brain and a mechanical body and one with a mechanical brain and a biological body[END_REF] . However, entwined around it, is a much more complex network of shifting relations. Drawing from examples of its use in narrative theory (Jameson, 1972, 2005 and Hayles, 1999), I have used the semiotic square as an analytical means of unraveling and interpreting those relations. The semiotic square (or Greimas' rectangle, from the name of the Lithuanian linguist Julien Greimas who theorized it) is a way of mapping two opposite terms (on the top) by adding a second opposition (on the bottom) with terms contradicting the first ones (the contradiction includes more meaning than the opposition: white is opposed to black, but non-black is much more than white). It is then possible to create complex terms by combining the first four elements (each combination corresponding to the different sides of the square) and generate new concepts that can help understand the narrative structure. Applying it to the narrative of Ganmu, I have thus positioned the opposition between organic life and machine on the upper corners. In contradiction to organic life, non-organic appears on the lower right corner, pointing out to information as a reified entity "that can flow unchanged between different material substrates" (Hayles, 54). It is a pattern that can be processed by computational functions so that "the nervous system and the automatic machine are fundamentally alike in that they are devices which make decisions" (Wiener, 33) based on it. Contradicting the machine is, on the lower left corner, the non-mechanical concept which refers to that which possesses its own will or telos [START_REF]end", in Aristotle's sense of an intrinsic purpose of organisms (for its own sake), opposed to an extrinsic or imposed goal (as when a machine performs the action designed by man)[END_REF] . It thus relates to emotion and stands in contrast to the non-organic corner (associated, as we have seen, with information and pattern) insofar as it brings randomness and chaos 25 . Because Ganmu deals about shifting boundaries in a highly technological environment, no characters fit precisely into one of the corners only but rather symbolize the hybridization of some of them. Arising from the combination of organic life and machine, on the upper side of the square, is the cyborg, in a subtle duality because it allows the biological brain/mechanical body as well as the mechanical brain/biological body arrangement. The deckmen, on the right side of the square, clearly symbolize the union of the machine and the non-organic as mechanical information-computing systems whose sole function is to embody Zalem control. On the lower side of the square, the AI Melchizedek represents the entanglement of computational functions and emotions, the emergence of an intrinsic teleology out of a silicon-based computer. Finally, characters such as Yûgo or Fogia properly fit the association of organic life with the non-mechanical corner on the left side of the square. They both retain their biological body and brain, and live their life according to their own will and emotions, should it be deemed as irrational or stupid. The best illustration of it is Yûgo having his dead brother's biological hand transplanted instead of his own (the reverse procedure of enhancement). From the mapping of those elements, we can now discern two main axes representing the natural/artefactual and matter/mind polarities. The first one is transversal, from fully biological humans with an intrinsic teleology to completely mechanical systems without will (though Kishiro ironically kept the suffix "man" in "Deckman" [START_REF]But doesn't Donna Haraway convincingly make the point that the cyborg figure "takes irony for granted?[END_REF] ). The second one, vertical, runs from the figure of the cyborgs as embodied entities (whether the body or the brain are seen as hardware) to the AI which represents disembodiment (interpreted as mind/spirit or software).

The semiotic square M A T T E R / E M B O D I M E N T / H A R D W A R E M I N D / D I S E M B O D I M E N T / S O F T W

A R E CYBORG DOUBLE DUALITY ZALEM CYBORG KUZU CYBORG DECKMAN HUMAN Yugo / Fogia ARTIFICIAL INTELIGENCE Melchizedek N A T U R A L A R T E F A C T U A L Crossing boundaries
However, the poles of the axis are not maintaining nor securing the usual "modern" divides. Indeed, the natural side of the square is taken up by humans, as natural beings, products from the evolutionary process, but thanks to whom the artificial side is made possible. Furthermore, apart from characters such as Yûgo and Fogia, the cyborgs of Kuzutetsu seem more human than, for example, the fully biological assistant of Desty Nova, Eelai. According to her, humanity's distinct trait rests in the flesh, through which humans feel pain and pleasure, and the ultimate evolution is immortality brought by nanotechnology. However, her definition exactly matches that of cybernetics whereby feelings and sensation are but a feedback loop that links a system to its environment through sensors, so that it can easily be reproduced in a cyber organism (Which echoes the fact that the word "organ" derives from the Greek "organon", meaning "instrument, tool"). However hedonistic it may sound (pleasure as the most important pursuit of humanity), it lacks the wisdom associated with it, equating emotion and feeling with bodily sensation only [START_REF]As Gally ironically points out, she is only "meat[END_REF] (thus departing from the "non-mechanical corner of the square). The embodied pole is not represented solely by organic life, but by the hybrid symbol of the cyborg; and the disembodied pole, the seemingly "boundless" and immortal spirit, concretely falls down from Zalem when "she" commits suicide [START_REF]The AI does possess a « core » ("hontai" 本体) that embodies its mind, so that death[END_REF] . There are thus no formal boundaries between one side and the other, but a constant flux shifting back and forth, a process which is best exemplified by Gally. Indeed, if we sketch her journey into the square in a diachronic way, it appears that she navigates "freely" within it, mediating between the opposites and establishing some union (or at least an intricate imbroglio). From an amnesic and "disembodied" brain at the beginning, it is through incorporation and embodiment that she is able set off on her quest for identity. The fighting technique she unconsciously masters reminds of the concept of "habitus" (in the sense defined by Marcel Mauss or Maurice Merleau-Ponty), as "body techniques" or actions anchored in the body by daily practice which eludes the self-conscious representation of such actions. Gally does not remember her skills, she does not even remember she has them, but her body does, even though it is mechanical. Her nanotechnologicaly-designed berserker body further blurs the distinction between embodiment and disembodiment, insofar as it seems to possess its own "will" [START_REF]When Ido found the body, its bearer's brain had already died but "it" didn't. It was as if "it" was waiting for a new "host" (Vol.1, 96-7). Nova subsequently understands that it "[END_REF] but still needs a brain to enter into symbiosis with. But then, who embodies who? who controls who? Gally remains in command but Zapan's brain is literally swallowed and consumed by it 30 . It also tangles up the natural and the artefactual in the sense that it is a "living machine" 31 born from a type of nanotechnology Nova is not familiar with. We may thus infer that instead of a mechanically-engineered molecular assembly by nano robots, it might rather have been designed by a chemically initiated self-assembly of molecules. It is then difficult to call it completely "artificial" (because it relies on the "spontaneity" of natural molecules) as much as it is improper to view it as a natural entity (there is a designer responsible for its construction). Gally, by later becoming a tuned agent, shifts to the right side of the square in that she relinquishes her free will to Zalem and mechanically performs the tasks she is given. It is only when she finally severs the enslaving link with Zalem that she finds love in the character of Fogia and decides to bring her journey to a conclusion by finding Nova and discovering the secret of the floating city. We can therefore assume that it is the lower left-hand corner that helps resolve the different oppositions: for Kishiro, "humanhood" is not so much defined by its physiology (even though embodiment is central in the narrative, it may take many shapes), nor by its rational logos and self-consciousness (which can be assumed by an AI computer or a bio-chip); but rather by its will and emotions, its perfectibility. However, this perfectibility is not seen as an individual ability-enhancement but as a moral struggle towards a better harmony where some mediation is established between technological achievement and society at large.

Reconfiguring the modern divides within an ethical frame

As we have mentioned it in the first part and further illustrated in the second, the technological achievements in Ganmu, stemming first from cybernetics and then from nanotechnology, tend to blur the oppositions between human and machine, matter and information, nature and artefact, organic and inert matter. However, Kishiro does not flatten them out boldly, but rather attempts at considering a new way of arranging them by way of mediation. This process is developed throughout the narrative, but it is the manga's conclusion which best exemplifies it. Brought to Zalem by Desty Nova, Gally penetrates with him the intimate core of the floating city and learns about the eugenics project led by Melchizedek. Unfortunately, the AI gets "out of mind", threatening to smash down Zalem on Kuztuetsu by committing suicide and destroying the skyhook. It is only the willing sacrifice of Gally which ultimately saves both microcosms. She reaches skyhook where she merges with the carbon nano-tubes of the column through a transmutation triggered by a chemical reactive that resonates with her nanotechnological body, and succeeds into securing the architecture of the orbital lift. The result, unpredictable according to Nova (because it depends on Gally's subconscious and latent potential), is quite surprising indeed. The fusion of a woman cyborg with an inert structure, keeping in mind that Gally's body was built by nanotechnology and that the orbital lift is made out of carbon nano-tubes, results in a huge blooming tree, nicknamed "nano-man/tree" 32 .

The symbolism of the nano-man/tree: metamorphosis and mediation

It is quite interesting to look a little more into the signification of this blooming tree in terms of the symbolic imagination, especially in the context of nanotechnology developments. Drawing on Gilbert Durand's work [START_REF]Giving an account, even a brief one, of the monumental work of Durand is beyond the scope of this article. I will only point[END_REF] , it is possible to identify the symbol of the blooming-tree with three intertwined groups of images. First, it reminds of the alternation between life and death through the agrarian cycle, in that the flora symbolism is often used to depict metamorphosis. Durand further adds that the heroine's death always gives birth to a plant, announcing her resurrection (342). Thermodynamics might then only be considered as the late rationalization of the primeval intuition that vital energy is conserved despite the passing of the seasons and the temporary death in winter. Secondly, and in close relation to the first group of images, is the symbolism of the mediation, the "conjunctio oppositorum" (Eliade 1989) we had mentioned in the first part of this article. By fusing with the skyhook's structure, Gally operates a conjunction between the living, the organic and the inert matter which transmutes the mechanical structure into an organic edifice from which she later emerges as a full flesh-and-bone woman. This process of transmutation establishes a link between alchemy's attempt at achieving in a short span of time what nature can do over a long period and nanotechnology's goal of overcoming human determinism, that is, ineluctable death. Mircea Eliade has convincingly written that "Alchemy has left much more to the modern world than a rudimentary chemistry, it has also transmitted its faith into the possible transmutation of Nature and its ambition to master Time. " (1956, 179) Thirdly, as a corollary of the first two groups of images, Durand notes (390) that the standing tree adds another dimension to the cyclical representation of life and death (through the agrarian symbolism) and the union of the contraries (the alchemical transmutation). Determinism is not overcome only through the reassuring confidence in some eternal return (repetition of the cycle which soothes out the irremediable loss of death) but because the bringing together of the opposites gives birth to a definitively new "someone/thing" (the nano-man/tree). The verticality of the tree echoes the promise of a progress transcending the ever-repeating cycle and prefigures the resurrection of life. Interestingly enough, it is nanotechnology that enables the "conjunction oppositorum" and the ultimate resurrection of Gally in the manga. It provides thus as a new way of configuring the interplay between nature and artifact, an invitation to reconsider, again, the definition of nature and that of the artifact.

A new definition of nature and artifact that needs moral consideration Indeed, the "nano-man/tree" symbolically epitomizes what Bruno Latour calls the "articulation of humans and nonhumans" into a "collective" whereby nature is not the world "out-there", ontologically safe and separated from the human but the result of a historical process through which it is defined and redefined according to the scientific or technological means available at a definite time. Our understanding of nature is based upon our ability to device tricks that give us access to it, so that the natural is articulated by the artefactual and vice-versa. The questions raised by this collective is "How many humans and nonhumans are to be taken into account?" and "Are you ready, and at the price of what sacrifice, to live the good life together?" (Latour, 1999, 297) Kishiro's narrative and graphic work echoes this question in a very interesting way. The seemingly dystopic world of Kuzutetsu is actually much more human than the aseptic society of Zalem because it does not assume a sense of perfection. This does not mean that an ethical assessment of technological development is made superfluous but it is precisely because the characters evolving within Kuzutetsu are not "perfect" that they can improve. Ultra-liberalism, gratuitous violence, hubris fed by individual enhancement do not escape Kishiro's critical eye but, as we have hinted at in the second section, Gally's picaresque journey is not so much a quest to grow stronger (in the physical sense) but rather a moral pursuit of self-betterment. In a form of answer to Latour's question, Gally's sacrifice symbolizes a kind of "promethean ethics" which, as the French philosopher Catherine Larrère has pointed out, is an altruistic move whose motivation may be called into question but which is intrinsically humanistic. When Prometheus stole fire from Zeus, it was to give it to mankind as a whole, not to let a few benefit from it to the detriment of others. Larrère opposes the promethean ethics to what she calls a "pelagian" ethics, from the name of the ascetic monk Pelagius who denied the original sin and asserted that man could reach salvation by its own means. She does not dismiss Pelagius' objective of individual self-betterment, but illustrates that an ethics based solely on individual perfectibility eludes the aspiration towards a larger Good that includes the social link. According to her, a self-centered and egoistic vision permeates the transhumanist hope for human enhancement by nanotechnology because it strives only for a better efficiency in a world defined as ultra-liberal and competitive. This is what Ganmu vividly characterizes through its very dark depiction of Kuzutetsu, where technological achievement serves only individual purposes (getting stronger, smarter, richer), and of Zalem where only a few benefit from apparently better living conditions. Gally only, but in a striking and conjuring stance, reminds of the need for a mediation between nature and society at large and, despite her strong individual will, highlights the importance of the social link. What Gally wishes for is that each and every human may be able to use his invisible wings to set off on his own journey 34 . Individual freedom is however closely intertwined with the collective in the sense that it goes along with some sense of responsibility and moral behavior. Building from cybernetics and the emerging nanotechnologies, Kishiro offers through his work a new way of envisioning nature and society where the proliferation of hybrids calls for a mediated and harmonious co-existence. Instead of rejecting hybridity as denaturing, or wholeheartedly embracing it as the ineluctable evolution of humankind, he makes the point that nature or "humanhood" are defined in a very complex way which is always in motion. It is therefore necessary to ponder over the possible new articulation between nature (as we construe it) and artifact:

Taking responsibility for the social relations of science and technology means refusing an anti-science metaphysics, a demonology of technology, and so means embracing the skilful task of reconstructing the boundaries of daily life, in partial connection with others, in communication with all of our parts. (Haraway, 181)

To conclude

From this very brief dive into a cyber-punk manga such as Ganmu, I hope I have been able to illustrate that science fiction can provide a very interesting interpretation of science worldviews, especially in the context of cybernetics and nanotechnology. The breakdown of ontological boundaries in the manga significantly echoes the blurring of the frontiers between "real" science and science fiction, as pointed out in the introduction. Even if this entanglement of science with science fiction seems of particular concern to some scientists, it does reveal that the practice of science is not confined in the ivory tower, that sciences and technologies are rather connected to society in what Latour calls a "network". Japan offers particularly compelling examples in the form of symposiums gathering well-known scientists and science fiction writers. One of the latest, the 65 th International SF Convention coupled with the 46 th Japanese SF Convention, held in 2007 in Yokohama, was entitled "Front Line of Science and SF" and brought together SF writers with Japanese researchers from various fields (robotics, cognition…). Sena Hideaki, who presided over the meeting, is himself a pharmacologist as well as a famous SF writer and emphasized that:

The bond between Science and SF will not break off at the end of the symposium. Both have whirled into a reciprocal stimulation right from the outset. While scientific achievements have continually given birth to fiction, the imaginative and creative power of fiction has always provided researchers with renewing motivation. The dialogue between Science and SF is still unfolding. 35 When speaking about science fiction and cyber punk, it is usually works by American authors that come into mind (most notably by William Gibson or Greg Bear). However, Japanese SF is also present: Hoshi Shin'ichi, one of the most prominent SF writer in Japan, has nicknamed the 1960's period the Golden Age of Japanese SF. According to him [START_REF] 経済成長がはじまったためか | 活力のベクト ル(方向)が変わったためか、一つの時代が終わってしまった。」 "It's story. Was it due to the advent of television and its spread in society, was it related to the beginning of economic growth, or simply[END_REF] , the improved conditions of living after the War stimulated the genre precisely because Japanese could focus on other matters than mere survival. Science fiction specialists Tatsumi Takayuki, Istvan Csicsery-Ronay Jr. and Christopher Bolton acknowledge the world cultural presence of Japanese SF from the end of World War 2, but they argue that it was not in the shape of prose literature that it met with international success, but rather through "newer" and more popular media, especially manga and anime (Bolton, et al. vii). Indeed these media (it's even truer of anime as it itself embodies new technology) add a powerful visual aspect to the narrative of science fiction, and aim at a much larger public (especially those who are not inclined to read prose but feel fascinated by science and technological development). Manga works like Ganmu, Akira or Ghost in the Shell sparked a worldwide enthusiasm that inspired other artists and helped trigger a reflection on the so-called post-modern blurring of the boundaries among other themes. new environment." (48) 14 The term was invented by Manfred and Nathan Kline in 1960 to refer to a human-machine system that would better withstand outer-space conditions of living. Céline Lafontaine has clearly shown that the concept of a cybernetic organism has stemmed from a militaristic project of performance enhancement (especially during the Cold War). 15 The idea of a space elevator dates back to as early as 1895 (Russian scientist Konstantin Tsiolkovsky proposed a "Celestial Castle" in geosynchronous Earth orbit attached to a tower on the ground), but the discovery of the carbon nanotube (CNT) in 1990 spurred scientific to consider the project seriously. See the 2000 Nasa headline: "Audacious & Outrageous: Space Elevators." <http://science.nasa.gov/headlines/y2000/ast07sep_1.htm> 16 Kishiro stresses out the fact that they are "Zalem robots", to be thus understood as servants of Zalem in the sense of Karel Čapek's robot in his play R.U.R. (the Czech word "robota" meaning literally work, labor, serf labor). 17 Non-neuronal cells of the brain which play a role in the nervous system. 18 Radio Frequency Identification Devices that contain an electronic product code to identify any individual object (and possibly "subject"). 19 "genuine" refers here to a physically unenhanced or unmodified human who retains his/her biological body. 20 As we will see later, Nova partakes of a double nature whereby he attempts at overcoming human determinism (seen as an oppression) by using trickery and deceit. His goal is to transcend the limits imposed upon human destiny (hence his name "Desty nova" can be interpreted as "new destiny") to attain a deathless world freed from human misery, but he also understands that perfection necessarily entails a part of shadow. As a former citizen of Zalem, his brain is a bio-chip but it presented some defect that prevented the embedded limiting system to work properly. He is not a "perfect" creation of Zalem eugenics so that this "defect" makes him closer to the cyborgs of Kuzutetsu. 21 Pointing out to the loss of energy of any thermodynamic system (including the universe) and the evening out of differences. As Kishiro states in a note from the 9 th volume, hot tea will cool down as time passes by, but the