Joint Learning of Intrinsic Images and Semantic Segmentation

Semantic segmentation of outdoor scenes is problematic when there are variations in imaging conditions. It is known that albedo (reflectance) is invariant to all kinds of illumination effects. Thus, using reflectance images for semantic segmentation task can be favorable. Additionally, not only segmentation may benefit from reflectance, but also segmentation may be useful for reflectance computation. Therefore, in this paper, the tasks of semantic segmentation and intrinsic image decomposition are considered as a combined process by exploring their mutual relationship in a joint fashion. To that end, we propose a supervised end-to-end CNN architecture to jointly learn intrinsic image decomposition and semantic segmentation. We analyze the gains of addressing those two problems jointly. Moreover, new cascade CNN architectures for intrinsic-for-segmentation and segmentation-for-intrinsic are proposed as single tasks. Furthermore, a dataset of 35K synthetic images of natural environments is created with corresponding albedo and shading (intrinsics), as well as semantic labels (segmentation) assigned to each object/scene. The experiments show that joint learning of intrinsic image decomposition and semantic segmentation is beneficial for both tasks for natural scenes. Dataset and models are available at: https://ivi.fnwi.uva.nl/cv/intrinseg

Introduction
Semantic segmentation of outdoor scenes is a challenging problem in computer vision. Variations in imaging conditions may negatively influence the segmentation process. These varying conditions include shading, shadows, inter-reflections, illuminant color and its intensity. As image segmentation is the process of identifying and semantically grouping pixels, drastic changes in pixel values may hinder a successful segmentation. To address this problem, several methods are proposed to mitigate the effects of illumination to obtain more robust image features to help semantic segmentation [1,2,3,4]. Unfortunately, these methods provide illumination invariance artificially by hand crafted features. Instead of using narrow and specific invariant features, in this paper, we focus on image formation invariance induced by a full intrinsic image decomposition.

arXiv:1807.11857v1 [cs.CV] 31 Jul 2018
Intrinsic image decomposition is the process of decomposing an image into its image formation components such as albedo (reflectance) and shading (illumination) [5]. The reflectance component contains the true color of objects in a scene. In fact, albedo is invariant to illumination, while the shading component heavily depends on object geometry and illumination conditions in a scene. As a result, using reflectance images for semantic segmentation task can be favorable, as they do not contain any illumination effect. Additionally, not only segmentation may benefit from reflectance, but also segmentation may be useful for reflectance computation. Information about an object reveals strong priors about its intrinsic properties. Each object label constrains the color distribution and is expected to reflect that property to class specific reflectance values. Therefore, distinct object labels provided by semantic segmentation can guide intrinsic image decomposition process by yielding object specific color distributions per label. Furthermore, semantic segmentation process can act as an object boundary guidance map for intrinsic image decomposition by enhancing cues that differentiate between reflectance and occlusion edges in a scene. In addition, homogeneous regions (i.e. in terms of color) within an object segment should have similar reflectance values. Therefore, in this paper, the tasks of semantic segmentation and intrinsic image decomposition are considered as a combined process by exploring their mutual relationship in a joint fashion.
To this end, we propose a supervised end-to-end convolutional neural network (CNN) architecture to jointly learn intrinsic image decomposition and semantic segmentation. The joint learning includes an end-to-end trainable encoderdecoder CNN with one shared encoder and three separate decoders: one for reflectance prediction, one for shading prediction, and one for semantic segmentation prediction. In addition to joint learning, we explore new cascade CNN architectures to use reflectance to improve semantic segmentation, and semantic segmentation to steer the process of intrinsic image decomposition.
To train the proposed supervised network, a large dataset is needed with ground-truth images for both image semantic segmentation (i.e. class labels) and intrinsic properties (i.e. reflectance and shading). However, there is no such a dataset. Therefore, we have created a large-scale dataset featuring plants and objects under varying illumination conditions that are mostly found in natural environments. The dataset is at scene-level considering natural environments containing intrinsic image decomposition and semantic segmentation groundtruths. The dataset contains 35K synthetic images with corresponding albedo and shading (intrinsics), as well as semantic labels (segmentation) assigned to each object/scene. Our contributions are: (1) a CNN architecture for joint learning of intrinsic image decomposition and semantic segmentation, (2) analysis on the gains of addressing those two problems jointly, (3) new cascade CNN architectures for intrinsic-for-segmentation and segmentation-for-intrinsic, and (4) a very largescale dataset of synthetic images of natural environments with scene level intrinsic image decomposition and semantic segmentation ground-truths.

Related Work
Intrinsic Image Decomposition. Intrinsic image decomposition is an ill-posed and under-constrained problem since an infinite number of combinations of photometric and geometric properties of a scene can produce the same 2D image. Therefore, most of the work on intrinsic image decomposition considers priors about scene characteristics to constrain a pixel-wise optimization task. For instance, both [6] and [7] use non-local texture cues, whereas [8] and [9] constrain the problem with the assumption of sparsity of reflectance. In addition, the use of multiple images helps to resolve the ambiguity where the reflectance is constant and the illumination changes [10,11]. Nonetheless, with the success of supervised deep CNNs [12,13], more recent research on intrinsic image decomposition has shifted towards using deep learning. [14] is the first work that uses end-to-end trained CNNs to address the problem. They argue that the model should learn both local and global cues together with a multi-scale architecture. In addition, [15] proposes a model by introducing inter-links between decoder modules, based on the expectation that intrinsic components are correlated. Moreover, [16] demonstrates the capability of generative adversarial networks for the task. On the other hand, in more recent work, [17] considers an image formation loss together with gradient supervision to steer the learning process to achieve more vivid colors and sharper edges.
In contrast, our proposed method jointly learns intrinsic properties and segmentation. Additionally, the success of supervised deep CNNs not only depends on a successful model, but also on the availability of annotated data. Generating ground-truth intrinsic images is only possible in a fully-controlled setup and it requires enormous effort and time [18]. To that end, the most popular realworld dataset for intrinsic image decomposition includes only 20 object-centered images with their ground-truth intrinsics [18], which alone is not feasible for deep learning. On the other hand, [19] presents scene-level real world relative reflectance comparisons over point pairs of indoor scenes. However, it does not include ground-truth intrinsic images. The most frequently used scene-level synthetic dataset for intrinsic image decomposition is the MPI Sintel Dataset [20]. It provides around a thousand of cartoon-like images with their ground-truth intrinsics. Therefore, a new dataset is created consisting of 35K synthetic (outdoor) images with 16 distinct object types/scenes which are recorded under different illumination conditions. The dataset contains intrinsic properties and object segmentation ground-truth labels. The dataset is described in detail in the experimental section. Semantic Segmentation. Traditional semantic segmentation methods design hand-crated features to achieve per-pixel classification with the use of an external classifier such as support vector machines [21,22,23]. On the other hand, contemporary semantic segmentation methods such as [24,25,26] benefit from the powerful CNN models and large-scale datasets such as [27,28]. A detailed review on deep learning techniques applied to semantic segmentation task can be found in [29].
Photometric changes, which are due to varying illumination conditions, cause changes in the appearance of objects. Consequently, these appearance changes create problems for the semantic segmentation task. Therefore, several methods are proposed to mitigate the effects of varying illumination to accomplish a more robust semantic segmentation by incorporating illumination invariance in their algorithms [1,2,3,4]. However, these methods provide invariance artificially by hand crafted features. Therefore, they are limited in compensating for possible changes in photometry (i.e. illumination). Deep learning based methods may learn to accommodate photometric changes through data exploration. However, they are constrained by the amount of data. In this paper, we propose to use the intrinsic reflectance property (i.e. fully illumination invariance) to be used for semantic segmentation. Joint Learning. Semantic segmentation has been used for joint learning tasks as it provides useful cues about objects and scenes. For instance, [30,31,32] propose joint depth prediction and semantic segmentation models. Joint semantic segmentation and 3D scene reconstruction is proposed by [33]. Furthermore, [34] formulates dense stereo reconstruction and semantic segmentation in a joint framework.
For intrinsic image decomposition, [35] introduces the first unified model for recovering shape, reflectance, and chromatic illumination in a joint optimization framework. Other works [36,37], jointly predict depth and intrinsic property. Finally, [38] exploits the relation between the intrinsic property and objects (i.e. attributes and segments). The authors propose to address these problems in a joint optimization framework. Using hand crafted priors, [38] designs energy terms per component and combines them in one global energy to be minimized. In contrast to previous methods, our proposed method is an end-to-end solution and does not rely on any hand crafted priors. Additionally, [38] does not optimize their energy function for each component separately. Therefore, the analysis on the influence of intrinsic image decomposition on semantic segmentation is omitted. In this paper, an in-depth analysis for each component is given.

Image Formation Model
To formulate our intrinsic image decomposition, the diffuse reflectance component is considered [39]. Then, an RGB image, I, over the visible spectrum ω, is defined by: In the equation, n denotes the surface normal, whereas s is the light source direction; together forming the geometric dependencies m, which in return forms the shading component S(x) under white light. Additionally, λ represents the wavelength, f c (λ) is the camera spectral sensitivity, e(λ) specifies the spectral Model architecture for jointly solving intrinsic image decomposition and semantic segmentation with one shared encoder and three separate decoders: one for shading, one for reflectance, and one for semantic segmentation prediction. The part in the dotted rectangle denotes the baseline ShapeNet model of [15].
power distribution of the illuminant, and ρ b represents the diffuse surface reflectance R(x). Then, using narrow band filters and considering a linear sensor response under white light, intrinsic image decomposition can be formulated as: Then, for a position x, I(x) can be approximated by the element-wise product of its intrinsic components. When the light source is colored, it is also included in the shading component.

Baseline Model Architectures
Intrinsic Image Decomposition. We use the model proposed by [15], ShapeN et, without the specular highlight module. The model is shown in the dotted rectangle part of Figure 1. The model provides state-of-the results for intrinsic image decomposition task. Early features in the encoder block are connected with the corresponding decoder layers, which are called mirror links. That proves to be useful for keeping visual details and producing sharp outputs. Furthermore, the features across the decoders are linked to each other (inter-connections) to further strengthen the correlation between the components. To train the model for intrinsic image decomposition task, we use a combination of the standard L 2 reconstruction loss (MSE) with its scale invariant version (SMSE). Let J be the prediction of the network andĴ be the groundtruth intrinsic image. Then, the standard L 2 reconstruction loss L M SE is given by: where x denotes the pixel coordinate, c is the color channel index and n is the total number of evaluated pixels. Then, SMSE scales J first and compares MSE withĴ: Then, the combined loss L CL for training an intrinsic component becomes: where the γs are the corresponding loss weights. The final loss L IL for training the model for intrinsic image decomposition task becomes: Semantic segmentation The same architecture is used as the baseline for semantic segmentation task. However, one of the decoders is removed from the architecture, because there is only one task. As a consequence, inter-connection links are not used for the semantic segmentation task. Furthermore, as a second baseline, we train an off-the-shelf segmentation algorithm [24], SegN et, that is specifically engineered for semantic segmentation task. To train the model for semantic segmentation, we use the cross entropy loss: where p is the output of the softmax function to compute the posterior probability of a given pixel x belonging to L th class, where L ∈ O x and O x = {0, 1, 2, · · ·, C} as the category set for pixel level class label.

Joint Model Architecture
In this section, a new joint model architecture is proposed. It is an extension of the base model architecture for intrinsic image decomposition task, ShapeN et [15], that combines the two tasks i.e. intrinsic image decomposition and semantic segmentation. We modify the baseline model architecture to have one encoder and three distinct decoders i.e. one for reflectance prediction, one for shading prediction, and one for semantic segmentation prediction. We maintain the mirror links and inter-connections. That allows for the network to be constrained with different outputs, and thus reinforce the learned features from different tasks. As a result, the network is forced to learn joint features for the two tasks at hand not only in the encoding phase, but also in the decoding phase. Both encoder and decoder parts contain both intrinsic properties and semantic segmentation characteristics. This setup is expected to be exploited by individual decoder blocks to learn extra cues for the task at hand. Figure 1 illustrates the joint model architecture. To train the model jointly, we combine the task specific loss functions by summing them together: The effect of the gamma parameters of Equation 6 and more implementation details can be found in the supplementary materials.

New Synthetic Dataset of Natural Environments
A large set of synthetic images is created featuring plants and objects that are mostly found in natural environments such as parks and gardens. The dataset contains different species of vegetation such as trees and flowering plants with different types of terrains and landscapes under different lighting conditions. Furthermore, scenarios are created which involves human intervention such as the presence of bushes (like rectangular hedges or spherical topiaries), fences, flowerpots and planters, and etc. (16 classes in total). There is a substantial variety of object colors and geometry. The dataset is constructed by using the parametric tree models [40] (implemented as add-ons in Blender software), and several manually-designed models from the Internet that aim for realistic natural scenes and environments. Ambient lighting is provided by real HDR sky images with a parallel light source. Light source properties are designed to correspond to daytime lighting conditions such as clear sky, cloudy, sunset, twilight, etc. For each virtual park/garden, we captured the scene from different perspectives with motion blur effects. Scene are rendered with the physics-based Blender Cycles 1 engine. To obtain annotations, the rendering pipeline is modified to output RGB images, their corresponding albedo and shading profiles (intrinsics) and semantic labels (segmentation). The dataset consists of 35K images, depicted 40 various parks/gardens under 5 lighting conditions. A number of samples are shown in Figure 2. For the experiments, the dataset is randomly split into 80% training and 20% testing (scene split).

Error Metrics
To evaluate our method for intrinsic image decomposition task, we report on mean squared error (MSE), its scale invariant version (SMSE), local mean squared error (LMSE), and dissimilarity version of the structural similarity index (DSSIM). DSSIM accounts for the perceptual visual quality of the results. Following [18], for MSE, the absolute brightness of each image is adjusted to minimize the error. Further, k = 20 is used for the window size of LMSE. For semantic segmentation task, we report on global pixel accuracy, mean class accuracy and mean intersection over union (mIoU).

Influence of Reflectance on Semantic Segmentation
In this experiment, we evaluate the performance of reflectance and RGB color images as input for semantic segmentation task. We train an off-the-shelf segmentation algorithm SegN et  framework may learn, to a certain degree, illumination invariance, it is not possible to cover all the variations caused by the illumination. Therefore, a full illumination invariant representation (i.e. reflectance) helps the CNN to improve semantic segmentation performance. Moreover, the confusion matrices show that the network is unable to distinguish a number of classes based on RGB input. Using reflectance, the same network gains the ability to correctly classify the ground class, as well as making fewer mistakes with similar-looking box and topiary classes.

Influence of Semantic Segmentation on Intrinsic Decomposition
In this experiment, we evaluate the performance of intrinsic image decomposition using ground-truth semantic segmentation labels as an extra source of information to the RGB images. We compare the performance of intrinsic image decomposition trained with RGB images (RGB) only as input and intrinsic decomposition trained with RGB images and ground-truth semantic segmentation labels (RGB + SegGT ) together as their input. As for RGB + SegGT , four input channels (i.e. RGB color image and semantic segmentation labels) are provided as input. The results are summarized in Table 2. As shown in Table 2, intrinsic image decomposition clearly benefits from segmentation labels. RGB+SegGT outperforms RGB in all metrics. DSSIM metric, accounting for the perceptual visual quality, shows the improvement on reflectance predictions, which indicates that the semantic segmentation process can act as an object boundary guidance map for reflectance prediction. A number of qualitative comparisons are shown for RGB and RGB + SegGT in Fig. 5.

Input RGB
RGB RGB+SegGT GT Fig. 5. Columns 2 and 3 show that RGB + SegGT is better in removing shadows and shading from the reflectance images, as well as preserving sharp object boundaries and vivid colors, and therefore is more similar to the ground truth

Joint Learning of Semantic Segmentation and Intrinsic Decomposition
In this section, we evaluate the influence of joint learning on intrinsic image decomposition and semantic segmentation performances. We perform three experiments. First, we evaluate the effectiveness of joint learning of intrinsic properties and semantic segmentation considering semantic segmentation performance. Second, we evaluate the effectiveness of joint learning of intrinsic property and semantic segmentation to obtain intrinsic property prediction. Finally, we study the effects of the weights of the loss functions for the tasks. Experiment I. In this experiment, we evaluate the performance of the proposed joint learning-based semantic segmentation algorithm (Joint), an off-the-shelf semantic segmentation algorithm [24] (SegN et) and the baseline of one encoder one decoder ShapeNet [15] (Single). All CNNs receive RGB color images as their input. SegN et and Single output only pixel level object class label predictions, whereas the proposed method predicts intrinsic property (i.e. reflectance and shading) in addition to the object class labels. We compare the accuracy of the models in Table 3. As shown in Table 3, the proposed joint learning framework outperforms the single task frameworks in all metrics. Further, visual comparison between SegN et and the proposed joint framework is provided in Fig. 6. In addition, confusion matrices are provided in the supplementary material. By analyzing the 3rd and 4th row of the figure, it can be derived that unusual lighting conditions negatively influence the results of the SegN et. In contrast, our proposed method is not effected by varying illumination due to the joint learning scheme. Furthermore, our method preserves object shapes and boundaries when compared to the SegN et model (rows 1, 2 and 5). Note that the joint network does not perform any additional fine-tuning operations (e.g. CRF etc.). Additionally, SegN et architecture is deeper than our proposed model. However,  Table 1 and Table 3, as the joint scheme enforces to augment joint features. Experiment II. In this experiment, we evaluate the performance of the proposed joint learning-based and the state-of-the-art intrinsic image decomposition algorithms [15] (ShapeN et). Both CNNs receive RGB color images as input. ShapeN et outputs only intrinsic properties (i.e. reflectance and shading), whereas the proposed method predicts pixel level object class labels as well as intrinsic properties. We train ShapeN et and the proposed method using groundtruth reflectance and shading labels on the training set of the proposed dataset.
We compare the accuracy of ShapeN et and the proposed method in Table 4.  Table 4, the performance of the proposed joint learning framework outperforms single task learning (ShapeN et) in all the metrics for reflectance (albedo) and shading estimation. Further, our joint model obtains lower standard deviation values. To give more insight on reflectance prediction performances, a number of visual comparisons between ShapeN et and the proposed joint framework are given in Fig. 7. In the figure, (the first two columns) it can be derived that the semantic segmentation process acts as an object boundary guidance map for the intrinsic image decomposition task by enhancing cues to differentiate between reflectance and occlusion edges in a scene. Hence, object boundaries are better preserved by the proposed method (e.g. the separation between pavement and ground in the first image and the space between fences in the second image). In addition, information about an object reveals strong priors about it's intrinsic properties. Each object label adopts to a constrained color distribution. That can be observed in third and fourth columns. Semantic segmentation guides intrinsic image decomposition process by yielding the trees to be closer to green and flowers to be closer to pink. Moreover, for class-level intrinsics, the best improvement (3.3 times better) is obtained by concrete step blocks, which have achromatic colors. Finally, as in segmentation, the joint network outperforms the single task cascade network, see Table 2 and Table 4.  Fig. 7. The first two columns illustrate that the proposed method provides sharper outputs especially at object boundaries than ShapeN et. The 3rd and 4th columns show that the proposed method predicts colours that are closer to the ground truth reflectance. The last column shows that the proposed method handles sharp cast shadows better than ShapeN et Experiment III. In this experiment, we study the effects of the weightings of the loss functions. As the cross entropy loss is an order of magnitude higher than the SMSE loss, we first normalize them by multiplying the intrinsic loss by 100. Then, we evaluate different weights on top of the normalization (SM SE × 100 × w). See Table 5 for the results. If higher weights are assigned to intrinsics, they both jointly increase. However, weights which are too high, negatively influence the mIoU values. Therefore, w = 2 appears to be the proper setting for both tasks. Table 5. Influence of the weighting of the loss functions. SMSE loss is weighted by (SM SE × 100 × w). w = 2 appears to be the proper setting for both tasks

Real World Outdoor Dataset
Finally, our model is evaluated on real world garden images provided by the 3D Reconstruction meets Semantics challenge [41]. The images are captured by Fig. 8. Evaluation on real world garden images. We observe that our proposed method capture better colors and sharper outputs compared with [15] a robot driving through a semantically-rich garden with fine geometric details. Results of [15] are provided as a visual comparison on the performance in Fig. 8. It shows that our method generates better results on real images with sharper reflectance images having more vivid and realistic colors. Moreover, our method mitigates sharp shadow effects better. Note that our model is trained fully on synthetic images and still provides satisfactory results on real, natural scenes. For semantic segmentation comparison, we fine-tuned SegNet [24] and our approach on the real world dataset after pre-training on the garden dataset. Since we only have the ground-truth for segmentation, we (only) unfreeze the segmentation branch. Results show that SegNet and our approach obtain 0.54 and 0.54 for mIoU and a global pixel accuracy of 0.85 and 0.88 respectively. Note that our model is much smaller in size and predicts the intrinsics together with the segmentation. More results are provided in the supplementary material.

Conclusion
Our approach jointly learns intrinsic image decomposition and semantic segmentation. New CNN architectures are proposed for joint learning, and single intrinsic-for-segmentation and segmentation-for-intrinsic learning. A dataset of 35K synthetic images of natural environments has been created with corresponding albedo and shading (intrinsics), and semantic labels (segmentation). The experiments show joint performance benefit when performing the two tasks (intrinsics and semantics) in joint manner for natural scenes. Acknowledgements: This project was funded by the EU Horizon 2020 program No. 688007 (TrimBot2020). We thank Gjorgji Strezoski for his contributions to the website.

S3 Results in Higher Resolutions
In this part of the supplementary material, the results of reflectance prediction in higher resolution are presented for better visual comparisons.

S3.1 Influence of Semantic Segmentation on Intrinsic Image Decomposition
In this experiment, we evaluate the performance of intrinsic image decomposition using ground-truth semantic segmentation labels as an extra source of information to the RGB color images. Qualitative comparison between predictions made from RGB images as input (RGB), against predictions made from RGB along with segmentation labels as input (RGB + SegGT ) are provided in higher resolutions in Fig. S2 and Fig. S3.

S3.2 Joint Learning of Semantic Segmentation and Intrinsic Decomposition
In this experiment, we evaluate the performance of the proposed joint learningbased and the a state-of-the-art intrinsic image decomposition algorithm ShapeN et [S3]. Both CNNs receive RGB color images as input. The proposed method provides sharper outputs especially at object boundaries and predicts colours that are closer to the ground truth reflectance. Higher resolution results are provided from Fig. S4 to Fig. S8.

S4 More Results on Real World Images
In this part, additional result of real world garden images are presented. The proposed method generates reflectance images with more vivid and realistic colors. Moreover, our method mitigates sharp shadow effects better and produces sharper images. Additional results of reflectance images are shown in Fig. S9, semantic segmentation results are shown in Fig. S10. For segmentation, the joint learning performs comparable to the baseline, yet we achieve sharper results.


Network Bandwidth Utilization Prediction Based on Observed SNMP Data

Bandwidth requirement prediction is an important part of network design and service planning. The natural way of predicting bandwidth requirement for existing network is to analyze the past trends and apply appropriate mathematical model to predict for the future. For this research, the historical usage data of FWDR network nodes of Nepal Telecom is subject to univariate linear time series ARIMA model after logit transformation to predict future bandwidth requirement. The predicted data is compared to the real data obtained from the same network and the predicted data has been found to be within 10% MAPE. This model reduces the MAPE by 11.71% and 15.42% respectively as compared to the non-logit transformed ARIMA model at 99% CI. The results imply that the logit transformed ARIMA model has better performance compared to non-logittransformed ARIMA model. For more accurate and longer term predictions, larger dataset can be taken along with season adjustments and consideration of long term variations.

Introduction
The rapid growth in the use of the Internet has led to huge and increasing demand for bandwidth, both international and domestic. Ensuring sufficient bandwidth in their network from core to the customer premises has been the constant challenge for network operators and service providers. In this context, timely and accurate prediction of bandwidth is very helpful to plan the network resources, expansion and upgrades on time so that bottlenecks and QoS degradations are avoided. This, in turn, is crucial for operators to attract, retain and regain customers. Hence, proper network traffic prediction is very useful for operators in initial planning as well as later on for upgrades and optimizations. To efficiently handle increasing subscriber base, service diversity and ensuing increase in bandwidth needs, service providers need to continuously monitor and predict traffic. For this research the reference data is taken from the utilization statistics of links in the Internet network of Nepal Telecom in the Far Western Region of Nepal. The data thus taken has been used to predict traffic using the method proposed here.

Literature Review
In a packet based network such as Internet, traffic flows through different links and nodes to reach from source to destination. The capability to predict future traffic demands can help improve the performance of network with timely planning for upgrades and updates. The self-similarity of network traffic has been studied in Local Area Network (LAN) [2]. To fit the bursty nature of the traffic ARIMA/GARCH model, which is a combination of a linear ARIMA (Auto-Regressive Integrated Moving Average) and non-linear Generalized Autoregressive Conditional Heteroskedasticity (GARCH) time series models, was proposed in [8]. According to [4], methodology for network resource management and network traffic congestion control using properly trained Artificial Neural Network (ANN) can better handle non-linear data relationship between the input and output patterns. The prediction model of network traffic based on Milti-Kernel Support Vector Regression (MKSVR) method showed that the proposed model can accurately describe the change trend of network traffic and hence improve the prediction accuracy of network traffic [6].
Several models have been proposed to predict network traffic. The short-term (a few minutes) forecast model using Auto-Regressive Moving Average (ARMA) with 1 sec time-scale data is proposed in [5]. The long-term (1 year) forecast model of Internet backbone traffic using ARIMA with 1 week time-scale data is proposed in [3]. The mid-term (1 day) forecast model using Autoregressive Conditional Heteroskedasticity (ARCH) model with 15 minute timescale data is proposed in [1]. This research focuses on three months prediction of the bandwidth utilization using 30 second time-scale data.

Methodology
The schematic model used in this research is as shown in Fig. 1.

Fig. 1: Research Schematic Model
For this research, real traffic data of 5 months for the Far Western Region network of Nepal Telecom was taken. The data is based on the SNMP capture for NMS visualization of the designated network nodes. This data is used as the training data for creation of the time series dataset that is used for the application of logit transformation and time series functions. The network links associated with the collected reference data is shown in Table 1. Daily aggregated traffic data has been taken of the Dhangadhi node for 6 months. This data is based on the NMS records which are aggregated every 30 seconds. The data collected was the inbound and outbound traffic at Dhangadhi node from the links given in Table 1. The inbound and outbound reference data thus collected are shown diagrammatically in Fig. 2   The framework for prediction based on collected data is shown in Fig. 4.

Fig. 4: Framework for Prediction
A univariate linear time series logit transformed ARIMA model is used for bandwidth utilization prediction of network traffic using the observed SNMP data.

Logit Transformation
Logit transformation is applied to the SNMP data 'x' to set the lower and upper bounds based on these limits. Time series data 'x' containing 'n' observations is transformed to time series data 'y' with lower bound 'a' and upper bound 'b' (10 10 bit/second) using the expression shown in Equation (1).

Network Bandwidth Utilization Prediction Based on Observed SNMP Data
The logit transformed SNMP data obtained after applying the transformation logic of Equation 1 are tested for seasonal adjustment using Seasonal Decomposition of Time Series by Loess (STL) in R programming.

Model Identification
The entire data set is split into training data (June to October) and testing data (November) for model validation. The purpose of model validation step is to determine accuracy of the model. For identification of the model, the selection the orders of p, d and q is performed using the "auto. arima" function in R programming which satisfied the Akaike's Information Criterion (AIC). The model with the minimum AIC is chosen as the best candidate model. The reason for the choice of AIC is because of the fact that AIC not only evaluates the fit between values predicted by the model and actual measurements, but also penalizes models with larger number of parameters.

Diagnostic Checking
The ARIMA diagnostic tests are run on the most promising of the model orders. Before fitting the accurate ARIMA model, the diagnostic checks of stationarity and normality are carried out.

Traffic Predictions
The network traffic is predicted after the order selection using "auto.arima" function. Then these predicted values are converted to the original scale using reverse logit transformation using Equation (2).
A one-step ahead prediction method is used recursively to obtain the subsequent values. The data of six months from June to November of a year of FWDR have been utilized to predict the network traffic.

Prediction Accuracy Check
A prediction error is the difference between the actual or real and the predicted value of a time series. For prediction accuracy check MAPE is used which is given in Equation (3).
where y t is actual traffic and f t is predicted traffic.
The normality test is carried out to test whether the data is normally distributed or not. When the data is normally distributed, then both mean or the median can be used as measures of prediction errors. In fact, in any symmetrical distribution the mean, median and mode are equal. However, in this situation, the mean is widely preferred as the best measure of prediction errors because it is the measure that includes all the values in the data set for its calculation and any change in any of the data will affect the value of the mean. This is not the case with the median or mode.

Results and Discussion
The entire data set is split into training data (June to October) and testing data (November) for model validation. The purpose of model validation step is to determine accuracy of the model . For identification of the model which satisfies the minimum Akaike's Information Criterion (AIC), the "auto.arima" of R programming is used. The function has the form where p is the number of autoregressive terms, d is the number of nonseasonal differences needed for stationarity and q is the number of lagged forecast errors in prediction equation.
The simulations have been carried out to identify the best ARIMA model and compare the errors of the other time series prediction models using R Studio package of R programming. For model validation, the entire data set is split into training dataset (June to October) and testing data set (November). The training for inbound and outbound are shown in Fig. 2 and Fig. 3 respectively. Similarly, the testing data (actual data of November) for inbound and outbound are shown in Fig.  5 and Fig. 6 respectively.

Fig. 5: Inbound Testing Data
An ARIMA model in training data set is implemented and found is a best model as per minimum Akaike information criterion using "auto.arima" function of R programming in forecast package. Seasonal components are also tested using "decompose" function in R and no seasonal components were found.   prediction method is chosen because of its minimum MAPE at 99% CI. The prediction of the traffic for the next time instant is performed recursively for the next day taking six months traffic data. This process is recursively performed for the next 90 days. The predicted inbound and outbound traffic time series plots for 90 days at 99% CI are shown in Fig. 11 and Fig. 12 respectively. A comparative study of the existing ARIMA model with logit transformed and non-logit transformed time series data is carried out to evaluate the prediction accuracy. It is found that the forecast error of logit transformed ARIMA model is significantly smaller than the non-logit transformed ARIMA model which clearly states the prediction efficiency of ARIMA model with logit transformed time series data. The logit transformed ARIMA model fitted the traffic data very well and this is confirmed by the goodness of fit test which are given by stationarity and normality. Such well-fitted statistical evidence is a solid empirical foundation for the prediction. The logit transformed ARIMA model reduces the MAPE by 11.71% and 15.42% respectively as compared to the non-logit transformed ARIMA model at 99% CI of inbound and outbound traffic.

Conclusion
A network bandwidth utilization prediction scheme is presented and analyzed using univariate linear time series model with ARIMA model after logit transformation. The logit transformed ARIMA model reduces the MAPE by 11.71% and 15.42% for inbound and outbound traffic respectively as compared to the non-logit transformed ARIMA model at 99 % CI. The reference data was available only for 6 months. It can be argued that if longer period of data is available, more accurate prediction for even longer duration can be achieved. For further research, larger dataset can be taken and season adjustments and long term variations can be taken into account to carry out predictions with longer periods with better confidence levels.


Betweenness centrality in urban networks: revealing the transportation backbone of the country from the demographic data

The theory of complex networks plays an important role in the modelling and analysis of processes in urban systems, for example, in studies of urban transport networks, the evolution of street networks, passenger flows in the city, etc. In this study we analyze a set of demographic data taken from official population census of Republic of Kazakhstan considering the complex network approach based on spatio-populational principles, namely, the estimation of the distance between cities and taking into account their populations. To determine the geopolitical importance of particular city we use very common network characteristics such as the degree of the node and the betweenness centrality. We show how the values of betweenness can reveal the main transport routes in the country and evaluate the wealth of transportation network.

Introduction
In recent years, the interest of researchers dealing with the problems of modeling various complex socio-economic systems is attracted by the network theory as an adequate mathematical tool capable of describing the structure and dynamics of complex processes in many real systems [1]. The formation of social, demographic, economic and other links inevitably leads to the formation of specific structures characterized by the presence of separate elements (or, as they are called in the theory of networks, nodes), between which various connections are established, which means the formation and development of various networks structures [2]. It should be noted that when building network models, communication between nodes can be very diverse and depend on the modeled object [3]. For example, links between network elements can be specified by existing material objects, for example, a network of railway or motorway connections, where links are determined by the presence of a corresponding path, power grids, etc [4]. However, links between network nodes can be defined in abstract space, such as an airline networks, when there are no physical connection between the nodes of the network (in this case between airports), and links are determined by the presence and intensity of air communication between two network vertices [5].
The theory of complex networks plays an increasingly important role in the modeling and analysis of processes in urban systems, for example, in studies of urban transport networks, the evolution of street networks, passenger flows in the city, and so on [6]. In fact, the city is a network of networks where different structures are "nested" in each other and closely intertwined, dynamically changing in the process of urban development [7]. At the same time, the majority of urban studies, which use the complex networks theory, are aimed at the analysis of particular cities or urban area, its economic and transport system. The use of network theory is also of great interest in the study of socio-economic indicators of cities, where a number of interesting results have also been achieved [8]. In particular, various approaches have been proposed to identify the links between cities that are of a socioeconomic nature [9]. At the same time, cities are also connected with modern communications and telecommunications, it has been shown that different types of networks are formed between cities, both hierarchical in nature and distributed, and interaction between nodes can be both cooperative and competitive.
Analysis of the available literature allows us to conclude that the network models are promising for understanding the evolution of the urban structure of a particular territory (country) and to identify the features of urban organization, in particular, to analyze the growth or degradation processes (due to the extinction of settlements) of the city network, the network characteristics during the administrative reorganizing processes (for example, transfer of the capital or changing the administrative division).

Overview of demographic data
Studies of real systems are often complicated by a limited set of statistical data available. This, in turn, encourages us to search for new approaches to extrapolate and to transform the existing data in more vivid representation. In this study we analyze a set of demographic data taken from official population census of Republic of Kazakhstan for three time points, 1989, 1999 and 2009. For each year, the data includes the population of each city, its name, geographical coordinates, and administrative affiliation.
We should note some basic features of the data under study. Firstly, the number of cities decreases during the considered 20 years from 292 to 139 in 2009. Secondly, this process is accompanied by the sharp growth of the average population of the city from 31,769 to 64,096. Such dynamics implies that the cease of the number of cities involved mostly the small towns together with overall outflow of people to regional centers. This also correlates with the relatively stable value of summary urban population of the country: from 9276510 in 1989 it decreased only to 8909333 in 2009.

The mathematical model
We consider approach to construct a mathematical model of the urban network, which is based on spatio-populational principles, namely, the estimation of the distance between cities and taking into account their populations. We are inspired by early works which attempted to estimate the interaction between cities in the gravity framework [10] together with the principles of spatial growth [11].
In this approach, in the first stage, for all pairs of cities i and j we calculate the weight of connection, wij, as where Pi is the population of the i-th city, Rij is the Euclidian distance between the i-th and j-th cities, i, j = 1..N, N -the total number of cities in the considered year. We also normalize the set of wij on the maximal value, so max  of the function? To answer this question, we will consider two fragments of the network in different scale, both including a node corresponding to the city of Almaty (Fig. 1 a, b). The Fig. 1 a shows a fragment of the nearest cities of Almaty. To the left is a fragment of a map with a marked characteristic scale of 20 km and four cities, three of which are rather small ones having the population of less than 40 thousand people. The diameter of blue circles is proportional to the population, Pi, of corresponding city (the number is also indicated on the map under the city name). The Fig. 1 b shows another situation -four large cities, located at large distances from each other (the characteristic scale on the map is 200 km).
On the middle and right panels on Fig. 1 we show the corresponding parts of the network with the values of the weights (marked by the thickness of the lines, and also by the numbers next to the corresponding link) for the two equations for wij. It is clearly seen that in the first case (the middle row), we have a very large spread of weight values, although it can be assumed that in such a compact urban agglomeration the strength of communication between cities should not be very different. Taking into account that we want to isolate and carry out the study of the formation of the backbone of strongly connected clusters of cities, we introduce nonlinear functions (by adding logarithm in the equation) that grow more slowly than the linear function, as the influence of the population of cities effects on the weight of links, as can be seen from the right figures. A similar situation arises when we consider large cities located at a great distance from each other, as shown in Fig. 1 b. Similarly, when considering the linear connection (middle figure), we get a significant spread in the link weight between cities located at approximately the same distance with each other, while closely located cities are characterized by significantly less ties. The right illustration shows the same network fragment constructed using relation (1), where the links are more homogeneous, which seems to be more correct. Based on these estimates, we have designed the equation (1) to calculate the weights of the urban network. However, to obtain more simple model, which can be easily studied using the complex network approach, we need to binarize the links between the cities. A large number of weights, wij, is characterized by negligibly small values, for example, for pairs of cities with small populations, located at a large distance, Rij, from each other. This is illustrated is Fig. 2, which shows the weight distribution of the 1989, when the number of settlements is the largest (N = 292), and hence the number of links. It should be noted that the distributions obtained for other years are qualitatively similar to the distribution presented in Fig. 2. On this basis, it is convenient to enter some threshold value Q, which defines the threshold weight of the connection, which is necessary to connect the two nodes (cities) with each other. The choice of threshold value can be determined from the weight distribution. One can see, that the most of it fits the power law (solid line) nicely. This marks the fact, that the obtained network demonstrates the scale-free property [12], which is essential for the majority of real networks [13]. However, the first point, corresponding to the negligibly small weights does not fits the power law, hence this weights can be removed from the system. More specifically, we choose the value of Q at which one of the networks nodes becomes disconnected, and restore the removed link. This vale of Q accurately vanishes the last bin of distribution as seen from Fig. 2. The corresponding networks constructed on the basis of our model are shown in Fig. 3 a-c. One can clearly see from them that the model reflects the connections between nearby cities that make up urban agglomerations (clusters) in the north of Kazakhstan, in the south it is tied to the former capital of Almaty, to the west, in the vicinity of the city of Uralsk, etc. There is also the establishment of chains of links through nodes (cities) which are located between such strongly connected clusters, being in good agreement with the form of transport arteries (roads and railways) in Kazakhstan making the connection between large cities through small towns located along roads. In our opinion, this indicates the correctness of the constructed model, which correlates, among other things, with the network of real transport flows.  Figure 3. Spatial visualizations of the obtained networks

Betweenness centrality in the urban network
To determine the geopolitical importance of particular city we use very common network characteristics such as the degree of the node and the betweenness centrality. The power of node i is a simple sum of all its connections: which allows to determine whether the given node is a structural hub aggregating connections with other elements, or a peripheral node. Nevertheless, this characteristic is very simple and does not allow to evaluate the real value of that or another network node as a transport hub. To assess the significance of a node in terms of transport flows we use the betweenness centrality, which is the number of shortest paths, gi, between any two other nodes that pass through the node i [14]: This type of centrality has shown itself very useful in urban studies, especially while studying street networks [15] or transportation paths [16].
First, we investigate how the betweenness centrality of the node correlates with its degree, i.e. number of links, connected to the node. The corresponding correlation plots are shown in Fig. 3 for  each network: (a) 1989, (b) 1999, (c) 2009. Easy to see, that quantities, which we investige are completely uncorrelated between each other. This occurs as very surprising result, because, intuitively, the nodes with high number of links should aggregate the shortest paths, which are going through them. This also is an essential property of scale-free networks, where hubs accumulate connections with other hubs [17]. On the countrary, in our urban network we observe the existence of small-degree nodes, which demonstrate very high value of betweenness. At the same time, we can also see nodes with high degree, which have almost zero shortest paths going through them. In 1989 nodes with high betweenness were located in the south of Kazakhstan and then spreaded to the northwest and north, roughly coinciding with the direction of the main transport routes of Kazakhstan connecting northwestern part of Kazakhstan, Russia and going up to the North, where a large number of cities are also densely concentrated. Moreover, the capital itself, Almaty (the orange dot in Figure 5), does not demonstrate a significant value of betweenness. Characteristics in 1999 and 2009. undergo significant changes related to a restructurization of urban network after the acquisition of independence by Kazakhstan. The changes that have taken place in this regard, first of all, the transfer of the capital and the rapid outflow of the population to big cities with the extinction of small settlements. It can be seen that the changes in the distribution of cities by number across the territory led to the fact that the nodes with the largest betweenness are now located in the north and central Kazakhstan. The southern region with the center in Alma-Ata becomes the periphery of the network and there are very few shortest paths going through it, basically, only those that are associated with this local fragment of the network. It is also pronounced that the role of Astana (the capital) is growing -the number of shortest paths passing through it has almost tripled since 1989. In general, if we consider the transfer of the capital from the point of view of optimizing communications within the urban network, then this was justified step and the place of the new capital was also chosen quite successfully, as indicated by the network analysis of the model.
The observed picture also gives us the answer, why the node betweenness is not correlated with its degree. The spatially-distributed urban network present itself the number of interconnected clusters of cities, which location is determined by complex historical process. This clusters of urbanization have a large number of link inside them, reflecting the strong communication between cities in the same region. At the same time, this dense clusters are interconnected by chain-like node structures, which mostly repeat the form of the main motorways of the country. Noteworthy, that the long chain of nodes connecting the south and west regions of the country lies alsmost at the location of the Great Silk Way. This chains of nodes are formed by the cities with rather small populations and, besides, we can observe the process of extinction of this nodes with time. Such dynamics can be considered as a marker of natural or political restructurization of transport paths inside the country, or degrading of transport communication between the regions. Figure 5. The betweenness centrality of the given networks. The size of the node reflects the value of betweenness. The capital is marked by orange color

Conclusion
Summarizing the results, we should note, that the information presented in this paper can be important for optimizing the structure of city networks, traffic flows, demographic and internal migration policies. Further development of mathematical models of such socio-geographic systems will allow to more accurately determine the geographic and demographic characteristics of a network of urban settlements of a particular region or country, and to develop recommendations to government based on rigorous mathematical approaches to improve the efficiency of such systems.


Identifying and evaluating layers of vulnerability – a way forward

Abstract “Vulnerability” is a key concept for research ethics and public health ethics. This term can be discussed from either a conceptual or a practical perspective. I previously proposed the metaphor of layers to understand how this concept functions from the conceptual perspective in human research. In this paper I will clarify how my analysis includes other definitions of vulnerability. Then, I will take the practical‐ethical perspective, rejecting the usefulness of taxonomies to analyze vulnerabilities. My proposal specifies two steps and provides a procedural guide to help rank layers. I introduce the notion of cascade vulnerability and outline the dispositional nature of layers of vulnerability to underscore the importance of identifying their stimulus condition. In addition, I identify three kinds of obligations and some strategies to implement them. This strategy outlines the normative force of harmful layers of vulnerability. It offers concrete guidance. It contributes substantial content to the practical sphere but it does not simplify or idealize research subjects, research context or public health challenges.

| 87 LUNA concept of vulnerability itself. The second encompasses a practicalethical sphere, dealing with the need to protect particular individuals and maintaining the relevance and usefulness of the concept of vulnerability in the evaluation of protocols. I will use the terms "conceptual sphere" and "practical sphere" to shorten the terminology. 5 The criticisms of Levine et al. that I hold valid are those that target the conceptual sphere of analysis (i.e., a vacuous use, a stereotyping effect, etc.) but I believe the concept is still very relevant from a practical sphere and these authors have not fully addressed this second sphere. "Vulnerability" has an important normative role and a practical and political force.
These challenges can be captured in two fundamental questions, that is: 1. How should vulnerability be understood? (conceptual question) 2. How should it be accurately used? (practical question) Even if these are two different spheres of discussion, they are closely related. Thus, if we cannot offer a fitting answer to the conceptual sphere, we will not be able to defend its practical relevance.
That is, if this concept is vacuous and stereotypes populations, it cannot be applied successfully. These serious conceptual problems should be solved in order to restore its utility and normative force.
At the same time, the two spheres of discussion present different problems. While the conceptual sphere focuses on the correct analysis and understanding of the concept of vulnerability, the second sphere poses how the concept of vulnerability can be applied to the ethical evaluation of research ethics and public policy design.
In "Elucidating the concept of vulnerability. Layers not Labels" 6 I proposed an answer to the first question through the metaphor of layers. This proposal has been accepted and even adopted as a valid strategy (Rogers, Mackenzie and Dodds 7 ; Meek Lange, Rogers and Dodds 8 ; Macklin 9 ; Den Hollander et al. 10 ). 11 Yet, in my earlier work I did not tackle the second question. Some of the bioethicists that endorsed my previous proposal challenged this lack of analysis and introduced a taxonomy as a way forward.
In this paper, I will first sketch my understanding of vulnerability and ways to deal with some conceptual sphere challenges. I will show that my proposal does not contradict other definitions or accounts of vulnerability. Rather, it includes them. Then, I will introduce a strategy addressing the second challenge that the practical sphere presents. I will identify some key relevant features of the concept of vulnerability (its dispositional character and structure and the possibility of a cascade effect). Finally, I will defend an analysis that implies a two-step process: an identification step and an evaluation step that includes a ranking process using the previously identified features.

| The traditional analysis
Since the Belmont Report was published there has been a tendency to label a particular subpopulation vulnerable. I called this approach "the metaphor of labels". 12 Affixing the label "vulnerability" to a particular subpopulation suggests a simplistic answer to a complicated problem. Research situations are often highly complex and influenced by the context and this approach overlooks this fact. Furthermore, a person or a group of persons may experience different kinds of vulnerabilities, and this complexity is ignored if we simply refer to a group of persons as vulnerable.
In "The Limitations of 'Vulnerability' as a Protection for Human Research Participants", Carole Levine et al. present several criticisms of the concept of vulnerability. I will focus on only some of the most relevant ones. 13 Carole Levine et al. say that "[…] the concept of vulnerability stereotypes whole categories of individuals, without distinguishing between individuals in the group who indeed might have special characteristics that need to be taken into account and those who do not". 14 These bioethicists are right when they point out this problem. We should first consider that stereotyping implies pinning a label and a content that cannot be easily removed on individuals or groups. And second, stereotyping or labeling persons can harm or wrong them. In addition, this strategy prevents us from identifying levels of vulnerability. The concept is presented in a dichotomous way: you are in or you are out. But among the vulnerable people some might be worse off than others. 15 Thus, the lack of flexibility of this conception of vulnerability should be underscored. It leads to a rigid view of the situation and implies conceptual and normative problems. 16 Another strong criticism that these bioethicists indicate says that so many categories of people are now considered vulnerable that virtually all potential human subjects are included. 17 They quote regulations, the CIOMS 2002 version among other documents, and then explain: "Under one or another of these rubrics, nearly everyone is vulnerable, especially since the benefits of research can never be 5 In the original article written in Spanish I called them "theoretical -ethical" and "politicalethical" but I was referring to the same distinction. I think this new terminology is clearer. guaranteed in advance […]. If everyone is vulnerable, then the concept becomes too nebulous to be meaningful". 18 And if this is the case, the concept is not relevant. We could argue that this criticism is too strong as we can deduce general moral obligations for all, even if we cannot deduce special moral obligations for special situations. This is right and it justifies the need for the general protection of human beings in research. However, when we use the notion of vulnerability in research, we try to provide special or specific protections or safeguards for some persons in particular and this cannot be achieved from this ontological perspective. Moreover, this view eventually "naturalizes" vulnerability: if we are all vulnerable and vulnerability is a "natural fact" that we all share, we need not avoid it or protect some persons from it. 19 The "labeling" metaphor, as well as the meaningless argument, shows deep problems at the conceptualethical level of analysis with important consequences at a practical level. 20 These criticisms need, first, an answer from the heart of the conceptual sphere.

| The structure and functioning of vulnerability: the layer metaphor
In "Elucidating the concept of vulnerability. Layers not Labels" 21 I proposed that the concept of vulnerability be conceived via the notion of layers. The metaphor of layers refers to the functioning of the concept. It suggests that there may be multiple and different strata and that they may be acquired, as well as removed, one by one. We do not face "a solid and unique vulnerability" that exhausts the category. There might be different vulnerabilities, different layers operating. These layers may overlap: some of them may be related to problems with informed consent, others to violations of human rights, to social circumstances, or to the characteristics of the person involved.
For example, we could say that the fact of being a woman does not in itself imply that one is vulnerable. A woman living in a country that does not recognize or is intolerant of reproductive rights acquires a layer of vulnerability (that a woman living in other countries that respect such rights does not necessarily have). In turn, an educated and resourceful woman in that same country can overcome some of the consequences of the intolerance of reproductive rights. Yet, a poor woman living in a country that is intolerant of reproductive rights acquires another layer of vulnerability. (She may not have access, for example, to emergency contraceptives and hence will be more susceptible to unwanted pregnancies.) Moreover, an illiterate poor woman in a country that is intolerant of reproductive rights acquires still another layer. And if she is a migrant and does not have her documents in order or if she belongs to the indigenous people, she will acquire increasingly more layers of vulnerabilities. She will suffer under these overlapping layers. 22 This concept of vulnerability is a contextual one. I understand it in the sense that the person may no longer be considered vulnerable if the situation changes. For example, a French working woman of reproductive age with middle-to-low income may not be vulnerable in a research protocol if she unwillingly gets pregnant (because in her country she has access to emergency contraception or an abortion at the public hospital if she wants). Whereas, if she is in El Salvador (where legal abortion is not allowed for any reason), that same French woman in that same protocol may acquire a layer of vulnerability. She does not become vulnerable, simpliciter.
In addition to the problems mentioned, the label metaphor makes two assumptions. First, it assumes a baseline standard for a default paradigmatic research subject (a mature, moderately welleducated, clear thinking, literate, self-supporting person) and second, it makes it possible to identify vulnerabilities in subpopulations in opposition to the paradigm or as defaults of the paradigm. This model is based on an idealization and simplification of research subjects. The problem lies in the nonexistence of these ideal subjects. Moreover, the existence of diverse research subjects within groups challenges the idea of homogeneous groups that share the category of vulnerability. The subpopulation approach assumes that there are necessary and sufficient conditions that populations must fulfil to be considered vulnerable. Instead, the layered way of viewing vulnerability allows it to target differences or variations within the group and to consider different kinds of safeguards or empowerment tools targeting these different features. 23 Thus, as there are no longer any labels or rigid categories, there are no fixed subpopulations -people that traditionally could be classified as vulnerable may not be so in a particular situation and vice versa. Interestingly, this conceptual analysis enables us to move away from "usual" or "typical" stereotypes. 24 In my proposal, 1) no single standard or ideal exists and there are multiple factors or sources of vulnerability; 2) they are deeply related to the context; and 3) vulnerability is not an essential property of the research subjects or groups per se. In sum, the subpopulation approach and the label metaphor are inadequate because they are tantamount to using the vulnerability concept as a mere slogan, categorizing and stereotyping persons. 18 Levine, op. cit. note 2, p. 46 (my emphasis). 19 Luna, (2009 a) op.cit. note 1, p. 128. 20 Ibid, pp. 127-8. 21 Ibid, p. 121-139. 22 Ibid, p. 127-128. 23 The layered position can work with individuals but also with subgroups within a population. The relevant point is to consider the different layers that are at play and not just take one characteristic to represent the group as a whole, be it for an individual or a subgroup.

| The meaning of vulnerability: definitions and content
In order to apply this concept properly we have to clarify other conceptual issues. "Vulnerability" is an elusive and slippery concept. We must distinguish between the structure and functioning of the concept from its "content" or characterization. We have just explained the structure of the concept. Its functioning is a relational and dynamic one, closely related to the situation under analysis. It is not a category or a label we can simply apply. The layered approach . 30 Yet, there may also be layers having to do with socio-economic conditions that could put these persons at greater risk or subject them to exploitation (and here we are following Zion's or even Hurst's definitions). 31 In addition, note that Hurst explains that "vulnerability as a claim to special protection should be understood as an identifiably increased likelihood of incurring additional or greater wrong". 32 Hurst shows how the concept relates to other concepts such as harms, wrongs, and normative claims. And again, the likelihood of harms and wrongs can be translated into different layers. These different definitions points to different layers, they show different factors that express vulnerability. Thus the structure of the metaphor of layers easily includes different definitions and we are not obliged to select only one of them. This complementary approach of my account has already been suggested by Macklin through a different angle. 33 Macklin criticized Hurst's proposal for its narrowness. She claimed that Hurst′s proposal only applies to research and clinical care.
Public health cannot be easily included. Macklin argues that Hurst's notion of "a claim to special protection" is too narrow. It cannot account for circumstances in which culture, custom, tradition, and laws make women unable to protect themselves. 34 Hurst's analysis has been, thus, "re-interpreted" by Ruth Macklin, who considers that my approach can complement Hurst's by contributing a contextual and relational aspect to this definition.
Another compatible approach is Kipnis's identification of some "characteristics that are criteria for vulnerability". 35 He endorses a taxonomy with six circumstances: cognitive, juridical, deferential, medical, allocational and infrastructural. For example, the cognitive circumstance asks if the potential participant has the capacity to deliberate on and decide whether or not to participate in the study; the juridical questions whether the potential participant is liable to the authority of others who may have an independent interest in that participation. 36 36 The others are: Deferential: Is the candidate-subject given to patterns of deferential behaviour that may mask an underlying unwillingness to participate? Medical: Has the candidate-subject been selected, in part, because he or she has a serious health-related condition for which there are no satisfactory remedies? Allocational: Is the candidatesubject seriously lacking in important social goods that will be provided as a consequence of his or her participation in research? Infrastructural: Does the political, organizational, economic, and social context of the research setting possess the integrity and resources needed to manage the study? Ibid, (p. G4).
to identify layers, but they might prove too narrow and rigid if we are to be bound only by them as laid out in a taxonomy.
Thus, my proposal accepts all these different definitions and accounts as they show the manifold angles of this layered approach to vulnerability.

| THE PR AC TI C AL S PHERE
The analysis based on layers was well received. In "Vulnerability in Research Ethics: A Way Forward" Meek, Rogers and Dodds say: "What is needed is an approach that, first of all captures Luna's insight that vulnerable research participants inhabit a context generated by the coming together of layers of vulnerability. The approach should make progress towards naming and classifying the layers while remaining sensitive to their possible interactions. Last, such account should identify vulnerability-related duties". 40 If we return to the two-sphere distinction I presented in the introduction, it would appear that the conceptual sphere is successful. However, there is still work to be done regarding the second sphere.
In "Why bioethics needs a concept of vulnerability?" Rogers, Mackenzie and Dodds 41 say: "We also outline a taxonomy of different kinds of sources of vulnerability which we think is helpful in further specifying the layered approach to vulnerability advocated by Luna body. Yet, no corset -despite its strings and fabric -will ever be able to "contain" her abundant flesh! 45 And this is the case when we try to classify layers of vulnerabilities. Taxonomies, like corsets, are not enough to categorize reality! The real world is too complex, The second step will evaluate how we can rank these different layers.
The previous identification of layers and their features will help in our evaluation. This team of bioethicists wrote several articles in the same vein: I will focus on these two articles: Rogers, op.cit. note 7; and Meek Lange, op.cit. note 8. 42 Rogers, op.cit. note 7, p. 19. 43 Meek Lange, op.cit. note 8, p. 337. 44 The authors sometimes use the term "typology" but they interchange both terms. 45 See the complete analysis and analogy in Luna 2015, op,cit, note 1. whether there are gender issues that may reveal other layers, and so on.

| First step
In addition, there are two useful features that may help not just to specify but to prioritize layers of vulnerabilities -as we will see below. The first relevant feature I want to underscore is that layers of vulnerability are dispositions and that the structure and relevant features of dispositions should be taken in consideration in an ethical analysis.
Being vulnerable or suffering a layer of vulnerability reveals that a person might be mistreated/abused/exploited under certain circumstances. Yet, a person need not be mistreated/abused/exploited to be viewed as having a layer of vulnerability or being considered vulnerable. 49 Moreover, if she is abused, mistreated or exploited, she is no longer vulnerable because she has already been harmed, abused, and so on. 50 It is the possibility of being harmed, mistreated or exploited that is relevant. Hurst's analysis, for example, also assumes this dispositional character when she speaks of an "increased likelihood of incurring additional or greater wrong". 51 Hurst′s proposal fits very well to the dispositional analysis of vulnerability that I am suggesting. This aspect of the concept of vulnerability which is highly relevant; has not been sufficiently analyzed and given the importance it should have (granting some exceptions such as Hurst proposal).
The notion and analysis of dispositions are not new. Yet I want to underscore that we need a careful consideration of the dispositional structure of layers of vulnerability because such analysis can help achieve a thorough ethical evaluation. A classic example of a disposition is the property of being soluble. A sugar lump has the disposition to solubility. That is, it is soluble if placed in water or a liquid. So, the disposition is latent until a specific stimulus condition triggers it.
This structure of dispositions dependent on a stimulus condition is quite relevant. In the case of sugar, the stimulus condition will be the event of introducing the solid lump of sugar into any liquid. In the case of layers of vulnerability, there can be many different situations that can trigger a layer of vulnerability. If that stimulus condition does not occur, that layer of vulnerability will never be actualized. I believe this idea of identifying a stimulus condition is crucial to the analysis and evaluation of layers of vulnerability. It is central if we want to correctly protect research subjects and persons. Similarly, just as we have identified the stimulus condition to make the lump of sugar soluble, we should identify the stimulus conditions that trigger a particular layer of vulnerability. If we could do so (and we can eradicate, avoid or minimize said disposition), we will probably be able to avoid the harmful consequences of layers of vulnerability. Yet, if a stimulus condition does not occur or is not probable, the person should still be considered to have a layer of vulnerability. But -as we will see below -when the REC or the policy designer evaluates this situation, that layer will not be a first priority. Rather, it will be other layers that can be triggered more easily. Thus, researchers, RECs, and policy makers should identify the stimulus conditions that can trigger the actualization of a layer of vulnerability in order to design suitable protection mechanisms.

| Relevant features to consider: Cascade layers
The second crucial feature is the cascading effect some layers may 50 In Spanish we have two different words for this difference: "vulnerable" has the same meaning in Spanish as in English and has this dispositional character; "vulnerado" means that the person has already been harmed. It is no longer a disposition but a fact that the person has already been harmed. For example, in public health ethics, the lack of an early diagnosis of a rare disease can be considered a cascade-vulnerability. 58 The lack of diagnosis implies lack of knowledge. The situation of uncertainty about these kinds of diseases may last for years with the accompanying anguish this implies for the patient, the family and the mistreatment of the patient (generating other layers of vulnerability). In addition, without a proper diagnosis the illness may evolve, making it impossible for the patient to access proper treatment when eventually diagnosed. Parents may make reproductive decisions, unaware of the high possibility that they might give birth to a child with the same problem, etc… Note that this layer of vulnerability can arise because the person faces a rare disease, and the lack of a proper diagnosis is one of the basic challenges to this kind of illness. Hence, neither abusive relations nor oppressive socio-political situations are the only situation to trigger this cascading feature.
Cascade layers can frequently be identified in the field of public health, given that they are associated with pre-existing conditions, context conditions (lack of access to health services, lack of protective laboral laws, etc), thus policies in the realm of public health can eradicate or minimize their effects. But in some cases, this cascading feature may prove very difficult to eradicate even with public laws or strategies. In the case of policy design and the elderly, we can see potentially different layers of vulnerability (economic, communicational, emotional, cognitive, and physical) 59 and some of them may be considered cascade-vulnerabilities. For example, consider this possible layer. Several marriages take place in today's families and new partners may not have an established relationship with the parents-in-law or elderly members of the family. In some cases, these new partners may not be very receptive to accommodating and accompanying older family members and this can give rise to a layer of vulnerability for some older people. If this happens, we can call this a relational layer: older people feel alone, isolated, and a burden on their families. If this relational layer occurs, it can also be considered a cascade layer as it can generate psychological harm, such as depression, which at the same time can lead to a loss of appetite or mobility, which, in turn, will probably engender fragility and physical illnesses, other morbidities, and the like.
As we will see in the next section, cascade vulnerabilities have a fundamental role in the process of evaluating the strength or damaging effect of layers. That is why I believe this distinction is key.
In sum, in this first step we should identify the: 2. Stimulus conditions, that is, determining the triggers of layers and the likelihood that they become manifest; 3. Cascade vulnerabilities, that is, layers that have a "cascade effect".

| The second step: evaluation and obligations
During the second step we should evaluate how to rank and prioritize these different layers. We should build on the first step. Based on the previous analysis, I propose a process to rank layers where the most harmful ones take priority. We should assess harms, wrongs and risks involved in the different layers (from physical to psychosocial, including the possibility of exploitation, dependency, abusive patterns, etc.). We should begin with the most harmful layers and move down to the less damaging ones. As we may have several other layers, we should begin by evaluating whether cascade layers exist.
Cascade layers are frequently the most harmful. They have a "domino" effect and can trigger several layers. We should give preference to layers with a cascade feature because they exhibit a differential strength and damaging power. As what we will try to do after is to avoid its harmful effects. However, we should weigh several factors: not only the damaging effect but the probability that it will happen.
We should balance the possibility of their occurrence and judge how harmful these layers are, as well as the situation under analysis and the threats involved. 60 This process can be understood from a coherentist model. There is no lexicographic order. 60 For example, if we have to evaluate the risks that some specific protocol imposes on the research subject, we should consider whether the intended protocol presents minimum risk (e.g., taking blood pressure using routine methods). In such case, the many layers may not be so important to remove when compared to a research protocol involving a liver biopsy or other kind of risks to participants. Thus the context or the situation will also be relevant for our evaluation. highly probable, they should take priority. These conditions are the ones that actualize the layers of vulnerability and will provoke actual harm. This is why they are the target. We should try to follow the established ranking. Nevertheless, sometimes this will not be feasible as reality can influence just how possible it is to follow strictly the preferred ranking. This ranking of layers should be taken as a procedural and preliminary guide in order to select from where to begin designing adequate safeguards.
Three kinds of obligations 61 can be applied to the previous ranking of layers and to the identification of stimulus conditions. Our first obligation is not to worsen the person's or group's situation of vulnerability (be this with a protocol intervention or with a public policy). Thus, we should avoid exacerbating layers of vulnerability. A second obligation consists of the eradication of layers of vulnerability. We should try to eliminate all layers. However, we can only demand this to a reasonable and possible extent.
Thus, the third kind of obligation minimizes layers of vulnerability.
If -when following the previous obligation-we could not eliminate or eradicate these layers, we should consider another alternative; we should find different strategies to minimize layers. We should use the previous ranking of layers as a guide. Finally, these obligations can be expressed through different strategies such as protections, safeguards, as well as empowerment and the generation of autonomy.
I believe this process can guide the deliberative thinking and evaluation of researchers, RECs, and public health policies. There is no fixed priority, but a guide that should help assess what our obligations are and from where to begin to tackle them.

| Putting concepts at work: a case
An example that shows how these concepts work is the following: a drug research project conducted with poor women in countries where abortion is illegal. In this case, the possibility of pregnancy functions as a cascade-vulnerability. As argued in Luna 2009 a 62 in the same situation, a woman of a high socioeconomic position will also acquire a layer of vulnerability.
There is moral harm when she has to undergo an illegal abortion (even if it is a safe process and she can afford it). In such case this layer will not have the cascade effect, while it will have it in the case of women with scarce resources. In the situation of these poor women, we can easily imagine the anguish of an undesired pregnancy conceived in the middle of a biomedical research project testing new drugs and the fear that the tested drug may have a teratogenic effect on the fetus's development (moreover if this happens early in pregnancy -teratogenic effects frequently occur in the first trimester). If the woman continues the pregnancy because she has no other option, the baby may be born with neurological or other defects that create multiple problems. If she opts for an illegal and unsafe abortion, other harmful consequences might arise: future sterility, morbidity and even mortality. Thus, we should identify whether there are stimulus conditions that may actualize this cascade vulnerability and assess how probable they are. If stimulus conditions are highly probable, this layer should be prioritized (not only is it very harmful with a cascade feature, but it is also quite probable). This is the case, for example, when these women are poor and lack socioeconomic resources, a formal education and, in particular, a sexual education without access to contraception.
If the REC considers the research highly relevant for these women and their community and it meets other ethical condi- vulnerability and its stimulus conditions, they should consider how not to trigger this cascade layer. They should assess whether they can eradicate or eliminate such dispositional layer. If they cannot, they should examine ways or strategies to minimize its occurrence.
Regarding the possibility of eradication, we should acknowledge that without safe policies to end pregnancies, the possibility of having an unwanted pregnancy will always exist despite special steps to avoid it. We should also understand that researchers or RECs cannot themselves change or issue a suitable law or policy.
(Yet, if this were the challenge for a public health agency, officers could take measures to eradicate it). So, inadequate sexual education, the lack of contraception and the lack of emergency contraception are some of the stimulus conditions that can give rise to this cascade layer of vulnerability. As was suggested, it may be quite difficult for RECs' members to eradicate this layer; yet, they should be able to minimize it. They should consider different strategies: effective protective measures or safeguards, as well as empowerment for these women. For example, a first set of necessary actions to achieve minimization are: explaining and educating about methods to avoid unwanted pregnancies, providing education on the use of contraception and access to it (explaining that it is better to use a double method and offer two birth control methods, as well as the possibility of emergency contraception). They can also propose other complementary actions: speaking with the relevant health officials, writing articles, giving interviews to the press to generate awareness of this restrictive and harmful reproductive health policy. I do not think these last suggestions should be obligatory for researchers or even for RECs; they function as supererogatory actions to be carried out to raise awareness of the problems involved. The RECs cannot eradicate that layer of vulnerability but by requiring researchers to implement at least the first set of protections mentioned, they can minimize it. However, if a REC thinks these strategies do not suffice (because these women will not be able to adhere to and use birth control owing to their partners' refusal or because it may lead to intra-family violence, etc.), the REC may 1) reject the protocol or 2) choose not to recruit these kinds of women into the study and set clear conditions for the participation of other women. 64 RECs should carefully examine how they evaluate layers, what kinds of provisions and safeguards should be in place for each layer. Even though I am offering a procedural guide, it is still essential that we make an exhaustive examination. My proposal forces us to make a moral deliberation and to avoid an easy and automatic analysis using checklists. But it provides valuable guidance on how and what to prioritize and try to tackle first. I believe that RECs, as well as policy makers in the case of public health, should make a thorough ethical analysis. They cannot rely on a list of "supposedly" vulnerable populations because -as was shown -this attitude risks stigmatizing these groups.

| FINAL THOUG HTS
This proposal provides a feasible and useful answer to the practical sphere. It suggests a middle-ground solution between a rigid taxonomy and just layers. It integrates standard definitions and accounts of vulnerability (Zion et al., Hurst, CIOMS, Kipnis).
It underscores relevant features of layers and offers a process for ranking and evaluating with explicit obligations to guide researchers, RECs and policy makers. This analysis generates obligations but it is clearer and simpler than the taxonomy-based proposal. It offers concrete guidance. It contributes substantial content to the practical sphere but it does not simplify or idealize research subjects, research context or public health challenges.

CO N FLI C T O F I NTE R E S T S TATE M E NT
The author does not have conflict of interest. The content is solely the responsibility of the author and does not necessarily represent the official views of the National Institutes of Health. 64 Obviously, this last step should be carefully evaluated as it can lead -in some cases -to overprotection (we have seen this reaction profusely in the case of research with pregnant women). 65 Meek Lange, op.cit. note 8 p. 336. 66 See my criticism of this in Luna, op.cit. note 24, p. 450.