text:
\section{Introduction}
The current generation has witnessed the abundance of satellite RS images acquired by sensors with varied resolution (both spatial and spectral). Additionally, it is nowadays possible to obtain images of a given area on the ground almost on a daily basis, thus assisting in (near) real-time monitoring of the Earth's surface. Notice that images acquired by sensors with different characteristics showcase complementary information regarding the underlying scene which, if fused properly, is expected to better analyze the ground objects. Historically, the notion of RS scene recognition is considered to be the prime ingredient of a number of RS applications which basically associates semantic meanings to the respective scenes. Precisely, such a semantic tagging invariably helps in applications related to military, urban planning, disaster mapping and many more.

The task of scene recognition (\cite{haralick1973textural}) is inherently different from the classical problem of image classification in RS which aims at predicting semantic land-cover classes at the pixel level (\cite{janssen1992knowledge}, \cite{blaschke2001s}). However, the focus on regions or sites is more important than analyzing the scenes at the pixel level in a number of applications and in particular for images with a very fine spatial resolution where very limited information is contained in the individual pixels. Nonetheless, both the tasks can be accomplished under the availability of different levels of supervision, namely, fully-supervised, semi-supervised, and unsupervised. The RS literature is traditionally rich in classification algorithms which, by definition, work on features extracted from the pixels/images. Mostly, such features or a combination of a number of base features are extracted explicitly from the image under consideration, thus giving rise to the notion of uni-modal feature extraction.  Majority of the work in current literature follows this approach to address the classification problem (\cite{camps2014advances}, \cite{li2014review}).

On the other hand, with the availability of comparatively low-cost sensors and due to the successful launch of several international satellite missions (Sentinel 1 and 2, for example), multi-modal data are rather easier to obtain nowadays. Considering the complementary information captured by diverse sources (multi-spectral and PAN, for example), the performance of a given classifier system can substantially be boosted if this rich information can intuitively be combined since such a multi-modal fusion strategy is expected to enhance the discriminative capabilities of the input representations. As a naive example, while the panchromatic (PAN) images show better spatial resolution, it is understood that the corresponding multi-spectral (MS) images depicting the same ground area are rich in spectral properties. Hence, a weighted combination of both the modalities is always encouraged than considering each of them separately for a typical scene understanding task. Likewise, multi-spectral or hyperspectral imaging involves many measurements along the visible spectrum for each pixel and helps in coarse to fine level object analysis while synthetic aperture radar (SAR) observations are nearly insensitive to cloud cover and provide a completely different aspect regarding the back-scattering of the surface materials.

Nonetheless, it is always expected that the individual image features which are likely to be fused should possess enough generalization proficiency. While for typical RS image classification, the spectral bands are in general considered as the feature descriptors, the extraction of image level features for scene recognition is non-trivial. The feature extraction paradigms have evolved from the hand-crafted ad-hoc features to the recent data-driven deep feature descriptors. Amongst the ad-hoc feature descriptors, SIFT, HOG, based local features are encoded with techniques like bag-of-visual-words, Fisher vector, VLAD etc. to obtain the image level representations (\cite{lowe2004distinctive}, \cite{dalal2005histograms}). On the other hand, global scene level descriptors (GIST) can directly accumulate the responses for a number of filters into a unified feature representation. Besides, primitive spectral and texture statistics like Gray level co-occurrence matrix (GLCM) and Local binary patterns (LBP) are also popular for RS images (\cite{su2008textural}, \cite{ojala2002multiresolution}). However, the advent of deep learning techniques has largely sidelined the ad-hoc descriptors for most of the visual inference tasks (\cite{lecun1995convolutional}, \cite{krizhevsky2012imagenet}, \cite{chen2014deep}).

In particular, the deep convolutional neural networks (CNN) based descriptors are capable of learning very generic to very specific abstract feature hierarchy from the images and thus possess improved discriminative capability. Deep CNN based techniques, which can deal with both the single as well as multi-modal data are prevalent in the literature (\cite{vaduva2012deep}, \cite{hu2015transferring}, \cite{zhang2016deep}, \cite{romero2016unsupervised}). CNN based feature learning from a single modality is straight-forward as it involves the learning of a number of consecutive convolutional kernels in an end to end fashion. However, the design is non-trivial while dealing with multiple modalities. Apart from learning abstract features from multiple modalities in terms of fully-connected networks, attempts are made to augment multiple modalities prior to feeding the features to the classification networks. A number of recent endeavors have dealt with the problem of multi-modal feature fusion for RS images as mentioned in (\cite{gomez2015multimodal}).

However, the existing multi-modal fusion based CNN architectures require the presence of all the modalities during training and testing which may turn out to be a hard constraint for a number of applications particularly involving time-critical disaster management systems. Due to the variations in the temporal resolution of different sensors, it may not be possible to obtain all the different images immediately after a disaster takes place. Notwithstanding this limitation, the consideration of different images remains instrumental for efficient and quick response arrangement. One likely scenario in this regard can be formulated when \textit{all the modalities are present during training while one or more modalities are missing during testing or on field implementation}.

Inspired by the aforementioned discussions, we propose a deep CNN based hallucination model for multi-modal image fusion where the model is trained with all the modalities where the image level abstract feature descriptors are learned in an end-to-end fashion. We basically work in the privileged information setting where we have additional data available during training procedure only but not while testing. However, we handle the absence of part of the input modalities during testing by separately modeling a hallucination network which is capable of approximating the missing modalities from the available ones on the fly.
Ideally, the considered problem definition shares some ideological similarities with the classical missing value problem in RS. However, here we are interested in a more generic learning based scenario driven by the paradigms of modality hallucination and knowledge distillation jointly. To the best of our knowledge, there is an existing endeavor for RS modality hallucination (\cite{kampffmeyer2018urban}), but none on distilled hallucination. However as opposed to (\cite{kampffmeyer2018urban}), we judiciously incorporate the paradigm of knowledge distillation in our framework (see figure \ref{fig:alphaAbalation} for effect of distillation), apart from following a more intuitive training strategy. We summarize the noteworthy contributions of the paper in the following:

\begin{itemize}
    \item We propose a multi-modal fusion based scene recognition framework for RS data. Although we consider the presence of all the modalities during model training, some of the modalities are considered to be absent during testing. In order to approximate the missing modalities from the available ones, we design an intuitive deep hallucination network.
    \item  By incorporating the generalized distillation framework in the hallucination module, we demonstrate how an improved hallucination performance can be achieved.
    \item We conduct extensive experiments on a large-scale dataset consisting of multi-spectral and PAN image pairs for the purpose of scene recognition and on a benchmark hyper-spectral dataset for pixel classification. We observe that the distilled hallucination network is capable of producing recognition accuracies comparable to a scenario when all the modalities are present during testing. In addition, we also show how the learning capacity of the hallucination network varies with changes in various model parameters.
\end{itemize}

\section{Related work}
Considering the focus of the paper, here we discuss briefly on i) multi-modal fusion based scene recognition in RS, and ii) knowledge distillation based approaches in machine learning, and iii) idea of hallucination networks.

The usefulness of employing data from multiple modalities was demonstrated by early works (\cite{bell1995remote}, \cite{pohl1998review}) well before deep neural networks became popular. This included thermal image fusion with LiDAR data for object recognition (\cite{chu1992image}) along with scene interpretation (\cite{clement1993interpretation}) and multi-source image classification (\cite{solberg1994multisource}) with optical and SAR data fusion to mention a few. In remote sensing, multi-modal fusion has been used for problems including urban mapping (\cite{gamba2013human}), oil slick detection (\cite{brekke2005oil}), forest studies (\cite{dalponte2008fusion}) and disaster management (\cite{dell2012remote}). There are also works on generating other modalities like Digital Surface Models (DSM) and Digital Elevation Models (DEM) (\cite{remondino2006image}) to name a few.

The notion of \textit{two-stream networks} is popular in fusing multi-modal information:  RGB and optical flow image for video analysis
(\cite{simonyan2014two}), Supervision transfer across RGB and depth (\cite{gupta2016cross}), spectral-spatial data integration in RS (\cite{hao2018two}), RGB and depth images for scene understanding (\cite{garcia2018modality}), to name a few. By design, the two-stream network architecture is comprised of two separate neural networks and outputs from each of them are fused using an appropriate fusion strategy. As expected, each branch of the two-stream network, or a stream, typically deals with instances depicting only one modality. Recently, a number of endeavors can be observed in the area of hyperspectral image analysis for efficiently integrating both the spectral and spatial information (\cite{hao2018two}).

Besides, \cite{hu2017fusionet} propose a two-stream convolutional network for urban scene classification using PolSAR and hyperspectral data. \cite{liu2018remote} use two steam network architecture for pan-sharpening using panchromatic in one stream and multi-spectral data in the other and later fuse the outputs using a concatenation and fusion layer. There have been other fusion approaches such as max, sum and average but all these can be subsumed by a learning based fusion strategy. It is found that the learning based feature fusion technique offers improved performance than the fusion strategies following max or average pooling. Recently \cite{audebert2018beyond} propose a deep learning based multi-scale approach towards multi-modal semantic labeling with LIDAR and multi-spectral data.

There have been previous works to address the missing modality problem using neural networks. \cite{tran2017missing} propose a data/modality imputation approach and this offline imputed dataset is used for training a classifier. We on the other hand propose a to hallucinate missing modality on the fly in an online fashion. \cite{ngiam2011multimodal} use all the modalities for feature learning step but use one modality for supervised training step after which the testing with that modality missing. We in contrast use all available modalities for training and only one for testing. \cite{wang2015deep} propose a autoencoder based architectures for learning representations for data and in turn maximize the correlation of features across modalities. The method cannot be trained end to end and has a network for representation learning with SVM or classifier being modeled henceforth on the projections. Our method is end-to-end trainable and due to this nature can be quickly fine tuned to new data for improved hallucination performance as per need.

Before commencing the discussion on the existing knowledge distillation approaches, we briefly mention the notion of model training with side information for better inference.
Precisely, training with privileged information consists of learning a student model in presence of additional information provided by a teacher model (\cite{vapnik2009new}). The idea is that the additional inputs provided by the teacher are expected to help the student to learn a better model. In the privileged information setting the additional inputs from the teacher are available to the student network only at the training time. At the test time the student network is expected to perform better inference pertaining to the initial teacher guided training process. \cite{luo2018graph} recently proposed a graph-based distillation method to learn in presence of side information for action detection and action recognition tasks in videos.

 \textit{Knowledge distillation} refers to the process of intelligently transferring the knowledge of an already trained, larger/complex learning module or an ensemble of several models to a rather smaller counterpart which is expected to be easier to comprehend and deploy. Knowledge distillation for machine learning models was first introduced by \cite{bucilua2006model} while \cite{hinton2015distilling} presented a more general approach towards distillation within the scope of a  feed-forward neural network and for the purpose of hand-written digit recognition. \cite{bucilua2006model} considers the operations directly on the logits obtained from the softmax layer for learning the smaller model and minimize the squared difference between the logits from cumbersome and the smaller model. \cite{hinton2015distilling} scale the final logits by a temperature parameter to obtain soft thresholds and these are used to train the smaller model. They also show that matching the logits as done by \cite{bucilua2006model}, is a special case of the technique they propose.

However, from the perspective of the distillation framework mentioned in \cite{hinton2015distilling}, the knowledge gathered by a deep network is not interpreted merely as the network parameters (weights and biases) obtained during training but also includes the final logits generated by the network corresponding to the inputs. Precisely, \cite{hinton2015distilling} propose a much general methodology for distillation of knowledge from a larger teacher network to a drastically smaller student network by using a parameter termed as the \textit{temperature}. Essentially, the output logits of teacher network are normalized with respect to the temperature parameter and subsequently the softmax operation is carried out on them in order to obtain a softer threshold which leads to an improved and more general knowledge transfer to the student network. The outputs of neural networks are typically class probabilities which are obtained by using the softmax function which converts the output logits $z_i$, computed for each class into a probability, by comparing it to other logits.

Consider a scenario where we have data from multiple modalities available during training, but while testing only one or a subset of these are available. \textit{Hallucination networks} are trained to learn to mimic the modality that is absent by training with the hallucination loss. This paradigm can readily be applied to RS applications considering that the missing data problem is quite frequent in many scenarios. However, there exists only one previous work on hallucination networks by \cite{kampffmeyer2018urban} which particularly hallucinates the missing Digital Surface Model (DSM) data in a remote sensing dataset and demonstrates improvement over an RGB based standard classification approach. However, the model is validated on a rather constrained scenario with limited samples and there has been no effort on knowledge distillation.

As opposed to the previous endeavor, ours is one of the initial approaches introducing the idea of knowledge distillation for missing data prediction in RS.
Combining the concept of distillation and privileged information we demonstrate a two-stream network that can be trained with two modalities and can hallucinate the missing modality at test time. Note that the data from the missing modality during testing is what we consider as the so-called privileged information in the training stage. We transfer the knowledge learned by the stream that is trained on the missing modality at test time to another network which is referred to as hallucination. The approach presented in this paper is influenced by the notion of generalized distillation framework \cite{garcia2018modality} which is considered for the purpose of human action recognition combining RGB and depth sensors. Apart from introducing the idea to the RS community, we specifically update their distillation framework with additional loss measure, which is showcased to boost the classification performance substantially.

\section{Proposed Approach}
\subsection{Preliminaries and Problem definition}
Let us consider a scenario where we have training data from multiple modalities: $(X_1, X_2, ... X_M)$, where $M$ denotes the total number of sensory modalities. In the classical supervised learning setting, one would prefer to design a classifier system by training the model with a subset or all of these $M$ features and subsequently evaluate the model on similar data items so as to preserve the consistency of the feature space. As opposed to this scenario, we aim to leverage data from all the $M$ available modalities to train a robust classifier but subsequently ensure that consistent or even improved generalization performance is guaranteed while only a subset of the $M$ modalities is available during testing. Precisely, let $X_i$ denote the available data samples from the $i^{th}$ modality with $Y$ being the corresponding class label while $X_j$ represents the data from missing sensor modality during testing considering that we deal with two different data sources in a given scenario. In this circumstances, we are interested in learning three functional mappings: i) $f_{comb}: \{X_i,X_j \rightarrow Y \}$ for the two-stream model, ii) $f_{hall}: X_i \rightarrow X_j$ for hallucination network, and iii) $f_{class}: \{X_i, f_{hall}(X_i)\} \rightarrow Y$ for the final classification.

For notational convenience, let $\mathbb{X}=\{(X_i^k,X_j^k, Y^k)\}_{k=1}^N$ formally represent $N$ image-label pairs from $Y^k \in \{1,2,\ldots,C\}$ scene themes where each image is captured by two separate sensors showcasing two completely different perspectives: $X_i$ and $X_j$ may denote the multi-spectral and PAN images for a given area or a pair of non-overlapping subsets of the original set of bands in hyperspectral image. For the next section, since we experiment with two data modalities, we assume $X_1$ and $X_2$ to represent panchromatic and multi-spectral data respectively and the network stream working with them are termed as PAN-Net and MS-Net for brevity. Note that we describe the proposed model using this setup, however, needless to mention, $X_i$ and $X_j$ may represent any different information sources in general. In the following, we describe the training process followed under generalized distillation for the proposed pipeline.

\begin{figure}
\begin{centering}
\begin{subfigure}[b]{0.465\textwidth}
   \includegraphics[width=0.8\linewidth]{images/trainingStep1.pdf}
   \caption{}
   \label{fig:step1}
   \vspace{20pt}
\end{subfigure}
\end{centering}
\begin{centering}
\begin{subfigure}[b]{0.465\textwidth}
   \includegraphics[width=1\linewidth]{images/trainingStep2.pdf}
   \caption{}
   \label{fig:step2}
   \vspace{20pt}
\end{subfigure}
\end{centering}
\begin{centering}
\begin{subfigure}[b]{0.465\textwidth}
   \includegraphics[width=1\linewidth]{images/trainingStep3.pdf}
   \caption{}
   \label{fig:step3}
   \vspace{20pt}
\end{subfigure}
\end{centering}
\hspace{15pt}
\begin{centering}
\begin{subfigure}[b]{0.465\textwidth}
   \includegraphics[width=1\linewidth]{images/trainingStep4.pdf}
   \caption{}
   \label{fig:step4}
   \vspace{20pt}
\end{subfigure}
\end{centering}

\caption{Training methodology as described in section 3.2, where multi-spectral data is unavailable during the testing phase. (a) Step-1: Both the networks are trained independently. (b) Step-2: Joint training of the two-stream network. (c) Step-3: Learning the hallucination network using the $L_{GD}$ and soft targets from frozen MS-Net, and finally (d) Step-4: Fine-tuning the hallucinated two-stream network with weights of the Hall-Net being frozen. The final step also represents how the network is deployed while testing. The striped networks represent that their weights are frozen in that step.}
\label{fig:trainingProcedure}
\end{figure}

\subsection{Training in detail}
We follow a four-step modular procedure for an intuitive training of our two-stream network. In particular, in the first two stages of training, we learn the \textit{teacher} network which is used in the following two steps in order to model the hallucination network. In the typical distillation setting, a \textit{teacher} network is the particular network whose outputs essentially guide the training of the second network which is termed as the \textit{student} network. Notice that with both $X_1$ and $X_2$ data streams being present, we initially learn separate network streams for each of the modalities followed by a joint learning combining both $X_1$ and $X_2$ in order to model $f_{comb}$. Subsequently, the stream for data modality that is known to be missing during testing is considered to be the teacher network and is deployed to learn the hallucination stream, $f_{hall}$ efficiently in conjunction with the generalized distillation loss. The trained $f_{hall}$, in turn, is used in the hallucinated two-stream network for classification which essentially has only the available modality as input and approximate the missing modality through $F_{hall}$ as if the testing is performed using both $X_1$ and $X_2$. The individual loss measures for each of the component networks are described in the following and Figure \ref{fig:trainingProcedure} depicts a flow for the training stages.

\subsubsection{Step-1: Learning the individual streams}
As aforementioned, we train each of the PAN-Net and MS-Net streams separately at the beginning. Essentially, these are designed as typical classification networks which perform classification solely based on the individual information source $X_1$ and $X_2$, respectively. Both of these networks are trained using cross entropy loss($L_{CE}$) for classification as given below:
\begin{equation}
    L_{CE}(Y^k, \hat{Y^k}) = - \sum_l Y_l^k \log\hat{Y_l^k}
\end{equation}
where, $Y^k$ and $\hat{Y^k}$ are the original and predicted labels in the typical one-hot vector fashion, respectively.

\subsubsection{Step-2: Learning the two-stream network}
In the second phase of training, we build our two-stream network (Figure \ref{fig:fullArch}), $f_{comb}$ which is trained using data from both $X_1$ and $X_2$. The first stream uses the architecture of the PAN-Net and the second stream uses the architecture of MS-Net, respectively. We fuse the two streams at the end and the new representations are classified following a learned late fusion strategy.

Specifically, the final class probabilities from both the streams are eventually concatenated and fed into a fusion layer. We employ a learned late fusion strategy, suppose $p_1, p_2 \in R^{N \times 1}$ are the probability score from the two streams respectively, we learn a set of fusion weights $w \in R^{N \times 2N}$, such that $ w *\!\ p = w_1 *\!\ p_1 + w_2 *\!\ p_2$, where $p \in R^{2N \times 1}$ is the concatenated probability vector. We want to learn a fusion layer such that,
\begin{equation}
    w = \arg\min_w \ell (p, Y; w) + \gamma \Vert w \Vert_2
\end{equation}
where $Y$ are the final labels and $\gamma$ is the regularization parameter. The symbol $ *\!\ $ represents the element-wise multiplication and $\Vert.\Vert$ is the standard $l_2$ norm.

For efficiently train the two-stream network, we first initialize the weights of the individual stream with the respective weights learned in Step-1 and the whole network is subsequently trained using cross-entropy loss. This strategy aids in better convergence in comparison to the random initialization.  This completes the learning of our teacher network for subsequent learning of hallucination network, which is the MS-Net for this description.

\begin{figure}[!t]
\centering
\includegraphics[scale=0.8]{images/twoStreamNetCompact.pdf}
\caption{Architecture of the two-stream network used for PAN-MS classification.}
\label{fig:fullArch}
\end{figure}

\subsubsection{Step-3: Learning the hallucination network}
In this step we train our hallucination stream, $f_{hall}$, using the generalized distillation process. Assume for instance that we wish to hallucinate the $X_2$ data stream while testing given data from $X_1$. In this case, we use the MS-Net architecture with data from $X_2$ as input which will act as a teacher network for the hallucination net which we alternatively term as the Hall-net. The Hall-Net or $f_{hall}$ follows the previous PAN-Net architecture and uses data from $X_1$ to generate corresponding the MS-Net activations. As shown in Figure \ref{fig:step3}, first we initialize the teacher network with the weights of MS-Net form Step-2 and freeze these weights. Next, we train the Hall-Net stream using the generalized distillation loss, to generate the multi-spectral counterparts for a given PAN instance.

In a standard knowledge distillation setting, we train the student model using a softer target distribution for the data samples via corresponding soft targets from the teacher network with the same high temperature in its softmax as the softmax of the student model. Let $z_i$ and $s_i$ be the logits and soft thresholds respectively, and $T$ is the temperature parameter. The outputs of the final layer in a neural network, before taking a softmax, are called the logits. The soft thresholds are obtained from the logits as follows,
\begin{equation}
s_i = \frac{exp(z_i/T)}{\sum_j exp(z_j/T)}.
\end{equation}
In the usual classification setting, the temperature parameter is typically set to $1$ to obtain the final probability distribution. But using a higher temperature value produce a softer probability distribution over classes which is employed during the knowledge distillation process.

Finally, this network is trained using the generalized distillation loss which comprises of the knowledge distillation loss term ($L_{KD}$) and hallucination loss term($L_H$) as given below. The hallucination loss is fundamentally a mean square error loss which minimizes the $l_2$ distance between the two soft thresholds, $s_{X_{2}}$ and $s_{Hall}$, of teacher and hallucination networks, respectively, which are obtained using a specific temperature.
\begin{equation}
    L_H = \Vert s_{X_2} - s_{Hall} \Vert^2
\end{equation}

The knowledge distillation loss, on the other hand, consists of two parts. Where first being the Kullback-Leibler divergence ($D_{KL}$) between the soft thresholds, $s_{X_{2}}$ and $s_{Hall}$. This is employed since we want to bring the two soft target distribution closer, allowing the student model to achieve better generalization. And the second part is a cross entropy loss between the hard labels, $z_{X_{2}}$ and $z_{Hall}$, from each of the two networks as we also want the hard target distribution to be as close as possible. The KL Divergence loss ($L_{KL}$) between the soft thresholds can be written as follows:
\begin{equation}
    L_{KL}(s_{X_{2}}, s_{Hall}) = D_{KL}(s_{X_{2}} \Vert s_{Hall})
\end{equation}

The complete knowledge distillation loss ($L_{KD}$) can now be formulated using the hyper-parameter $\lambda$ as follows:
\begin{equation}
    L_{KD} = \lambda T^2 L_{KL} + (1-\lambda) L_{CE}(z_{X_{2}}, z_{Hall}).
\end{equation}

Combining the hallucination and knowledge distillation loss we write the final generalized distillation loss ($L_{GD}$) as below:
\begin{equation}
    L_{GD} = \alpha L_{KD} + (1-\alpha) L_{H}
\end{equation}
where $\alpha$ is chosen to weigh in both these components suitably.

Note that the performance of the initial MS-Net stream is what we would ideally want the hallucination network to achieve eventually.

\subsubsection{Step-4: Learning the hallucinated(pseudo) two-stream network}
As the final phase of training and after having trained the $f_{hall}$ network to hallucinate the missing data stream $X_2$, we use it in the two-stream network, $f_{comb}$ from Step-2. We replace the missing modality stream of $f_{comb}$ with $f_{hall}$ to learn the final $f_{class}$ network. In particular, we initialize the first stream with weights from PAN-Net in Step-2 and the second stream with weights from Hall-Net learned in the previous step. Notice that the weights of the hallucination stream are frozen and subsequently we only fine-tune this hallucinated two-stream network with cross-entropy loss. This network is considered to be ready to perform classification with missing $X_2$ during decision making. Again, note that the performance of the initial two-stream network form Step-2 will be what we would ideally want to achieve with this hallucinated two-stream model.

\subsection{Testing}
The testing is straightforward given the hallucinated two-stream model from Step-4. We input $X_1$ in the PAN-net and $f_{hall}(X_1)$ in the HALL-net, respectively. The final classification scores are obtained by following the aforementioned late-fusion strategy mentioned in our original two-stream model (Step-2).

\section{Experimental Evaluations}
\subsection{Dataset description}
We use two datasets for analyzing the efficacy of the proposed hallucination model: the first one containing panchromatic images of aerial scenes along with the corresponding multi-spectral data. This dataset consists of a total of $80000$ PAN-MS pairs from eight different land-cover categories \cite{li2018learning}. The images are acquired by the GF-1 panchromatic and GF-1 multi-spectral sensors, respectively. The panchromatic images are of size  $128 \times 128$, with a spatial resolution of 2m and single spectral band. The respective multi-spectral data has a resolution of $64 \times 64$, with a resolution of 8m, and consists of a total of four spectral bands. The classes considered here are aquafarm, cloud, forest, high building, low building, farmland, river, and water.

The second is the Pavia University hyperspectral datacube acquired by the ROSIS sensor\footnote{\url{www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes}}. It is taken over the Pavia region in northern Italy and consists of $103$ spectral reflectance bands with $610 \times 340$ pixels each post discarding bands with unimportant information contents. The datacube bands have a geometric resolution of $1.3$ meters and the area contains pixels from nine land-cover classes namely, Asphalt, Meadows, Gravel, Trees, Painted metal sheets, Bare Soil, Bitumen, Self-Blocking Bricks and Shadows

\subsection{Model architectures}
Here we summarize the typical experimental setup considered for both the datasets. For experiments on the PAN-MS dataset, we alternatively consider the PAN and MS modality to be missing during testing. For the hyper-spectral data, we consider a subset of bands to be missing and aim at hallucinating them given the remaining bands.

For a given set of experiments conducted on the PAN-MS data for scene classification, we train one of the streams of our initial two-stream network with the panchromatic band and the second with multi-spectral data and provide only the panchromatic data for testing. Needless to mention, we focus to hallucinate the multi-spectral data for each test sample in this case. Notice that both of the network streams considered here are based on convolutional neural networks as our inputs are image objects. By design principle, the PAN-Net consists of four convolutional layers followed by four fully connected layers. Note that the structure is fixed heuristically.
On the other hand, the MS-net contains three convolutional layers and three subsequent fully-connected layers. In a separate set of experiments, we hallucinate the PAN images given the MS data-cubes.

Our network is inspired from the alexnet architecture \cite{krizhevsky2012imagenet} and since our dataset is relatively small we use a rather minimalistic version of the same. We tested the method with 3,4, and 5 convolution layers and find that the performances of the models do not drastically vary. Hence, we report the performance of the model with 3 hidden convolution layers. The late fusion scheme is standard in multi-stream deep learning models. One of our goals here was to keep the model simple and yet delivering good performance.

For the experiments concerning the hyper-spectral data, we use first $52$ bands (according to indices) of Pavia University datacube as our first sensor modality ($X_1$) and the goal is to hallucinate the subsequent $53$ bands during testing. Likewise in another set of experiments, we reverse the interpretation of both the streams and hallucinate the first $52$ bands while testing with only the last $53$. As far as the network design is concerned, we use a $3$ layer network consisting of fully-connected layers for modeling the two network streams. As opposed to the PAN-MS case where the input data dimensionality are different, both the streams, in this case, follows identical architecture.

\subsection{Experiments protocols}
For the case of PAN-MS data set, in Step-1, the PAN and MS network streams are individually trained with a learning rate or $3 \times 10^{-4}$. The same parameter values are also used to train the hallucination stream as well. For Step-2 where the initial two-stream network is trained and Step-4 where the hallucinated two-stream network is trained, the learning is performed with a learning rate of $1.5 \times 10^{-4}$. Each of the networks is trained for 100 iterations while the initial two-stream network is trained for 200 iterations. For experiments on HS data, each of the two streams and hallucination stream is learned with a learning rate of $6 \times 10^{-4}$ and the initial and hallucinated two-stream networks are learned using a learning rate of $3 \times 10^{-4}$.

The performance is measured using the overall and class-wise accuracies of classification. We would like to highlight that we would ideally like the performance of Hallucination network to be as close to the performance of networks stream for the missing modality. Similarly, we would like the performance of the hallucinated two-stream network to be higher than that of the available modality stream but of course as close as possible to the original two-stream network trained with both the data modalities.

We also extensively study the effects of various parameters in modeling our network, namely, $\alpha$, $\lambda$ and $T$ along with the network depth.

\begin{table}[!t]
\caption{\label{tab:expt1}Performance of different networks (in percentages) on PAN-MS dataset. The mean and standard deviation of overall accuracy over 10 runs is provided.}
\centering
\begin{tabular}{|p{1.8cm}|@{}c@{}|c|}
\hline
Classifier & Class-wise Accuracy & Overall Accuracy\\
\hline
PAN-Net &
\begin{tabular}{c}
Aquafarm : 96.64\\\hline Cloud : 99.36\\\hline Forest : 94.13\\\hline High building : 95.48\\\hline Low building : 94.86 \\\hline Farm land : 90.36\\\hline River : 94.29\\\hline Water : 99.46
\end{tabular}
&  95.82 (0.20)\\
\hline
MS-Net &
\begin{tabular}{c}
Aquafarm : 95.27\\\hline Cloud : 99.87\\\hline Forest : 95.52\\\hline High building : 95.04\\\hline Low building : 98.00\\\hline Farm land : 92.41\\\hline River : 94.59\\\hline Water : 99.46
\end{tabular}
& 96.16 (0.31)\\
\hline
Two-Stream \newline Net &
\begin{tabular}{c}
Aquafarm : 96.03\\\hline Cloud : 99.59\\\hline Forest : 96.84\\\hline High building : 96.82\\\hline Low building : 98.57\\\hline Farm land : 95.13\\\hline River : 93.51\\\hline Water : 99.88
\end{tabular}
& 98.23 (0.15)\\
\hline
\end{tabular}
\end{table}

\begin{table}[!t]
\caption{\label{tab:expt2}Performance of different networks (in percentages) on PAN-MS dataset while hallucinating each of the streams. The mean and standard deviation of overall accuracy over 10 runs is provided.}
\centering
\begin{tabular}{|p{1.8cm}|@{}c@{}|c|}
\hline
Classifier & Class-wise Accuracy & Overall Accuracy\\
\hline
MS \newline Hall-Net \newline (T=10) &
\begin{tabular}{c}
Aquafarm : 97.27\\\hline Cloud : 99.12\\\hline Forest : 97.24\\\hline High building : 99.24\\\hline Low building : 92.38\\\hline Farm land : 90.59\\\hline River : 96.21\\\hline Water : 99.90
\end{tabular}
& 96.12 (0.03)\\
\hline
MS \newline Hallucinated \newline Two-Stream Net &
\begin{tabular}{c}
Aquafarm : 96.30\\\hline Cloud : 99.46\\\hline Forest : 96.32\\\hline High building : 96.65\\\hline Low building : 94.68\\\hline Farm land : 93.47\\\hline River : 97.30\\\hline Water : 100
\end{tabular}
& 96.78 (0.08)\\
\hline
PAN \newline Hall-Net \newline (T=10) &
\begin{tabular}{c}
Aquafarm : 95.53\\\hline Cloud : 99.67\\\hline Forest : 97.20\\\hline High building : 94.83\\\hline Low building : 97.90\\\hline Farm land : 92.79\\\hline River : 93.71\\\hline Water : 99.72
\end{tabular}
& 96.42 (0.22)\\
\hline
PAN \newline Hallucinated \newline Two-Stream Net &
\begin{tabular}{c}
Aquafarm : 96.65\\\hline Cloud : 99.91\\\hline Forest : 97.36\\\hline High building : 95.21\\\hline Low building : 98.66\\\hline Farm land : 94.18\\\hline River : 94.10\\\hline Water : 99.90
\end{tabular}
& 97.01 (0.11)\\
\hline
\end{tabular}
\end{table}
\subsection{Results on PAN-MS Data}
We perform our experiments on the panchromatic and multi-spectral pair dataset with the convolutional network architecture as shown in figure \ref{fig:fullArch}. We use different training ratios for our experiments and study the performance variations with respect to them. The results are shown in Table \ref{tab:expt1} and Table \ref{tab:expt2} for hallucinating multi-spectral and panchromatic streams, while considering 50\% of the entire data for training, from which 5\% is used for validation. In these tables, the PAN-Net and MS-Net refer to the individual network streams for each of the modalities as in Step-1. The Two-stream Net is the network from Step-2, where the two individual streams are fused. The MS Hall-Net and PAN Hall-Net refer to the Hallucination networks from Step-3 for hallucinating Panchromatic and multi-spectral data streams respectively. The proposed Hallucinated two-stream Net is the two-stream network from Step-4 comprising of the hallucination stream in place of the missing modality. The table \ref{tab:expt2} shows overall and class-wise accuracies of each of the streams with hallucination, from where it is evident that the multi-spectral hallucination network has indeed learned to approximate the missing modality and achieves a comparable accuracy as its teacher network which is the original multi-spectral stream. The results presented are mean and standard deviation of the of the values.

And also the hallucinated two-stream network constructed with this hallucination stream gives us a higher overall accuracy and class-wise accuracies in comparison to the panchromatic stream. Similar observations can be made from table \ref{tab:expt2} about the panchromatic hallucination network which gives higher overall accuracy than the multi-spectral stream alone. Note that the performance of the two-stream network trained in Step-2 acts as the upper bound to what the hallucinated two-stream network is expected to achieve.

We can observe from results in table \ref{tab:expt2} that for multi-spectral data hallucination, out of the eight classes, the Cloud class is best classified by the each of the streams with an accuracy going over $99\%$ in each case. The Water class, on the other hand, is best hallucinated by the Hallucination Net and the hallucinated two-stream network achieves a performance close to the original two-stream accuracy for it. Also, we can observe that the class farmland is the most difficult to classify and is extremely difficult to hallucinate as well with its classification accuracy being the least among the available classes but still has a performance jump of around $+1\%$ in the hallucinated two-stream network in comparison to the PAN-Net. For panchromatic data hallucination from results in table \ref{tab:expt2}, the Cloud class is still best classified by all the networks, while the class River remains most difficult to hallucinate having a lower classification accuracy than even the Farm land class. The classification of the Farmland class suffers in this case too as the performance of the initial PAN and MS network streams is far from being good for it in particular. For this case, the jump in classification accuracy of Farmland class is around $4\%$ from MS-Net to hallucinated two-stream model.

\begin{figure}[!t]
\centering
\begin{subfigure}[b]{0.44\textwidth}
   \includegraphics[width=1\linewidth]{images/panNetLossCurve.png}
   \caption{}
   \label{fig:metics1}
\end{subfigure}
\begin{subfigure}[b]{0.44\textwidth}
   \includegraphics[width=1\linewidth]{images/msNetLossCurve.png}
   \caption{}
   \label{fig:metrics2}
\end{subfigure}
\begin{subfigure}[b]{0.44\textwidth}
   \includegraphics[width=1\linewidth]{images/twoStreamNetLossCurve.png}
   \caption{}
   \label{fig:metics3}
\end{subfigure}
\begin{subfigure}[b]{0.44\textwidth}
   \includegraphics[width=1\linewidth]{images/finalNetLossCurve.png}
   \caption{}
   \label{fig:metrics4}
\end{subfigure}
\caption{Training performance curves for (a) Panchromatic network, (b) Multi-spectral network, (c) Two-Stream network and (d) Final Hallucination network.}
\label{fig:trainingCurves}
\end{figure}

We further present the error and accuracy curves of the four networks while training namely, PAN-net, MS-net, Two-Stream Net and the hallucinated two-stream net, for 100, 100, 200 and 100 training epochs, respectively. We consider the best performing network snapshots over validation set during each of these training processes for deployment and use in subsequent stages. It can in Figure \ref{fig:trainingCurves} be observed that the final network converges quickly since it merely requires slight fine-tuning which is performed at a lower learning rate in comparison to the other networks.

\subsection{Abalation Study}
\subsubsection{Effect of varying network parameters}
We subsequently conduct experiments to study the effect of changing various hyper-parameters of our model and also consider different training-test splits in order to assess the learning capability of our network under different levels of supervision. We defined $\alpha$ to be the weighing factor of knowledge distillation loss in the overall loss term. Figure \ref{fig:alphaAbalation} shows how by increasing the parameter $\alpha$, the accuracy of the hallucination network increases first and then decreases for higher values of $\alpha$. From this, we can easily infer the advantage of the inclusion of the knowledge distillation loss term in our objective function. In particular, $\alpha = 0$ implies only the hallucination loss term is used and $\alpha =1$ is for when only the knowledge distillation loss term is prioritized. We can observe from figure \ref{fig:alphaAbalation} that the knowledge distillation loss results in an improved hallucination network. As desired, a weighted combination of these two loss measures provides an improved performance in terms of classification accuracy in comparison to just using one of them. Essentially, we observe a substantial improvement in the learning capability of the hallucination network by using the proposed generalized distillation technique instead of resorting to only the hallucination loss.

\begin{figure}[!t]
\centering
\includegraphics[scale=0.39]{images/alphaAbalation.png}
\caption{Effect of $\alpha$ on classification accuracy (in percentages) of Hallucination network on PAN-MS dataset.}
\label{fig:alphaAbalation}
\end{figure}

\begin{figure}[!t]
\centering
\includegraphics[scale=0.39]{images/lambdaAbalation.png}
\caption{Effect of $\lambda$ on classification accuracy (in percentages) of Hallucination network on PAN-MS dataset.}
\label{fig:lambdaAbalation}
\end{figure}

In figure \ref{fig:lambdaAbalation} we can observe how the increase in the value of $\lambda$ parameter changes the learning of our hallucination stream. As a part of the knowledge distillation loss, the parameter $\lambda$ weighs the constituent losses of the $L_{KD}$. If $\lambda = 0$, only soft threshold are used for knowledge distillation and in the case of $\lambda = 1$, only the hard targets are used for the distillation process. We can observe that soft targets give us a better hallucination performance in comparison to only the hard targets. In this case, too, we obtain a much better hallucination network by using a weighted combination of these losses instead of just resorting to using hard targets for distillation loss.

\subsubsection{Effect of varying temperature}
Although the temperature parameter is difficult to tune we found a trend in the same on averaging the results across multiple runs. We repeated the proposed MS hallucination process for multiple temperature values ranging from 1 to 100 while $\alpha$ and $\lambda$ were each set to $0.5$. The results of this are presented in Fig. \ref{fig:temperatureAbalation}. We can observe from the figure that the soft threshold obtained at $T=15$ give best hallucination performance for this particular case. Another observation is that the performance initially increases with increase in temperature but quickly starts dropping with further increase.

\begin{figure}[!t]
\centering
\includegraphics[scale=0.39]{images/temperatureAbalation.png}
\caption{Effect of $T$ on classification accuracy (in percentages) of Hallucination network on PAN-MS dataset.}
\label{fig:temperatureAbalation}
\end{figure}

\begin{table}[!t]
\caption{\label{tab:expt4}Performance of different networks on PAN-MS dataset (in percentages) on reducing network depth by one each of convolution and fully connected layer in each of the network streams.}
\centering
\begin{tabular}{|p{5cm}|c|}
\hline
Classifier & Overall accuracy\\
\hline
PAN-Net & 93.97\\
\hline
MS-Net & 95.33\\
\hline
Two-Stream Net & 96.43\\
\hline
MS Hall-Net (T=10) & 93.74\\
\hline
Hallucinated Two-Stream Net & 95.50\\
\hline
\end{tabular}
\end{table}

\subsubsection{Effect of varying stream depth}
The next experiment involves varying the capacity of each our streams. Specifically,  we reduce the number of layers in each network stream and observe how this affects the hallucination and hallucinated two-stream network performance. The corresponding performance measures are tabulated in table \ref{tab:expt4}. We observe that reducing the network depth affects the learning capacity of the hallucination network along with all of the other networks. The results are shown for the case of hallucinating multi-spectral data stream. We observe that the performance of initial MS net drop by 1.5\% in comparison to the results from table \ref{tab:expt1} and that of the hallucination net drops by 3\% due to the reduction in network parameters.

\subsubsection{Effect of varying training data size}
For this study, we use three different training-test splits to carry out the training process and report the observed changes in final classification performance. In particular, we use training sets comprising of 20\%, 50\% and 80\% samples from the entire dataset. Table \ref{tab:valAbalation} shows the accuracies for varying training ratios for each of our networks, where we hallucinate the multi-spectral data stream. As expected, it can be observed that the accuracies of each of the networks increase with an increase in training set size. The hallucination network also improves with an increase in data and as a result, the hallucinated two-stream network always showcase a higher accuracy measure than considering solely the PAN-stream.

\begin{table}[!t
    \centering
        \caption{\label{tab:valAbalation}Performance of different networks on PAN-MS dataset (in percentages) for varying training sizes.}
    \begin{tabular}{c|c|c|c|}
        \cline{2-4}
         & \multicolumn{3}{|c|}{Training Ratio} \\
        \cline{2-4}
         & 20\% & 50\% & 80\%\\
        \hline
        \multicolumn{1}{|c|}{PAN-Net} & 95.05 & 95.60 & 96.50\\
        \hline
        \multicolumn{1}{|c|}{MS-Net} & 93.95 & 96.43 & 96.60\\
        \hline
        \multicolumn{1}{|c|}{Two-Stream Net} & 95.51 & 97.97 & 98.32\\
        \hline
        \multicolumn{1}{|c|}{MS Hall-Net (T=10)} & 92.78 & 96.20 & 96.48\\
        \hline
        \multicolumn{1}{|c|}{Hallucinated Two-stream Net} & 94.63 & 96.87 & 97.79\\
        \hline
    \end{tabular}
    \label{table::supervised1}
\end{table}

\subsection{Results on Hyperspectral Data}
We divide the Pavia University hyperspectral datacube into two parts and used them to train each of the streams in order to hallucinate the other half of the bands in the datacube. The same train-test split is employed here as the PAN-MS data. The results for hallucinating the second (53) and the first half (52) of the bands are shown in table \ref{tab:expt3}, respectively.

\begin{table}[!t]
\caption{\label{tab:expt3}Performance of different classifiers on Pavia hyperspectral datacube (in percentages) while hallucinating the first 52 and last 53 bands. The mean and standard deviation of overall accuracy over 10 runs is provided.}
\centering
\begin{tabular}{|p{5cm}|c|}
\hline
Classifier & Overall accuracy\\
\hline
Stream one & 85.29 (0.14)\\
\hline
Stream two & 93.57 (0.22)\\
\hline
Two-stream Net & 95.82 (0.15)\\
\hline
Hallucination network for \newline band 53-103, (T=15) & 85.12 (0.17)\\
\hline
Hallucinated Two-stream Net \newline for band 53-103 & 86.04 (0.25)\\
\hline
Hallucination network for \newline band 1-52, (T=15) & 93.33 (0.07)\\
\hline
Hallucinated Two-stream Net \newline for band 1-52 & 94.24 (0.12)\\
\hline
\end{tabular}
\end{table}

From the tables, it can be noted that these two sub-cubes offer fairly complementary contributions to the final classification accuracy. Similar to the case of PAN-MS dataset, we can see that the hallucination network has successfully learned to mimic the missing bands of the datacube to a considerable extent. Besides. we can also observe that the hallucinated two-stream network offers an improved performance in comparison to the single stream networks trained on either half the datacube. This indicates that we can obtain an improved performance with a lesser number of bands using our hallucination architecture than a unimodal classifier.

\section{Conclusions}
We propose a hallucination network model in this paper which can deal with the problem of missing data modality during testing in a typical remote sensing classification task. It is imperative that information fusion from multiple sources provide complementary insights to a geographical area which helps in better characterization. However, in many situations, some of the modalities may remain absent. Our approach is capable of approximating such missing modalities during the inference stage. Ideally, we train our model with all the available modalities, thus giving rise to the notion of learning with privileged information. We validate the proposed framework on a large-scale PAN-MS dataset for scene recognition and a hyperspectral image for pixel classification where the effectiveness of model hallucination is established through extensive experimentation. We plan to extend this model to handle multi-temporal data as a future endeavor.

\bibliographystyle{IEEEbib}

redpajama_set_name: RedPajamaArXiv


Muscat: The Omani economy is on track for strong growth in the short run sustained by buoyant oil prices along with positive momentum in the growth non-oil activities, the Central Bank of Oman (CBO) announced here yesterday.
This upbeat assessment comes on the back of a healthy 8.7 per cent nominal growth in GDP during 2017, the apex bank said in its 2017 Annual Report published here yesterday.
However, the positive outlook is somewhat tempered by concerns linked to developments in global oil markets, political tensions and other issues that may have an adverse impact over the medium-term, it warned.
The agreement to cut oil production between OPEC and non-OPEC oil producers, the CBO Report noted, has been extended up to the end of 2018, while some supply shock is anticipated on account of expected drop in oil production from Venezuela (due to political instability and lack of investment) and uncertainty with regard to oil supply from Iran after withdrawal from the nuclear agreement by the USA.
At the same time, it said, shale production from the USA has displayed inability to compensate for a drop in oil production by OPEC and non-OPEC oil producing countries. Even OPEC countries would find it difficult to jack up oil production within a short-time as the investment in exploration remained subdued because of protracted low oil prices. Global demand for oil, on the other hand, is expected to remain robust over the foreseeable future as the world economy has gained further momentum with fairly broad-based recovery.
"Oman also continues to invest in exploration of oil and gas to enhance the production level and the Khazzan gas project once developed fully (phase I has already come in full steam and the work on phase II has already commenced), would add considerably to the natural gas production," the report said.
Also of beneficial significance to the economy, the CBO said, is the 'Tanfeedh' programme which continues to concentrate on five sectors for implementing specific projects and initiatives with a potential for diversification.
"Various other policy efforts, such as public-private partnership (PPP), enactment of Foreign Investment Law and Bankruptcy Law, privatization of government companies etc, are underway to propel non-oil economic activities and private sector-led growth," it said.
Furthermore, going beyond 2020, the Vision 2040 also focuses on economic diversification to develop a more diversified, dynamic, globally integrated and competitive economy for ensuring sustainable growth over the long-run.
The financial sector, the report said, has also been actively participating in nurturing the non-oil sector in the economy by providing requisite funding and other services, including promoting Small and Medium Enterprises (SMEs).
For its part, the CBO has also implemented certain policy measures recently, enabling banks to support higher economic activities in the Sultanate through meeting the requirements for credit and other banking services.
"In the above backdrop, the growth outlook for the Omani economy appears to be robust over the short-term. Nonetheless, some downside risks to the outlook over the medium-term may emanate from tightening of global financial conditions and uncertainty about oil prices, especially on account of progress with regard to the alternative fuels and increase in supply by the countries outside the cartel of OPEC and non-OPEC oil producers," the Central Bank noted.
According to the report, Omani crude oil price averaged at $51.3 per barrel in 2017, while the 2017 budget had assumed average oil prices at $50 per barrel. Consequently, oil & gas revenues grew by 19.6 per cent, increasing their share in total revenues to 55.6 per cent in 2017. On the other hand, non-oil & gas revenues dropped by 4.6 per cent in 2017 mainly due to a significant decline in capital repayments.
On the other hand, the expenditure declined for the third year in a row during 2017, reflecting the government's commitment towards fiscal consolidation. The decline in government expenditure by 4.9 percent during 2017 was contributed by moderation in both current and investment expenditure. The spending norms have been restructured aiming at a reduction in deficit and eventually restoring the fiscal balance, the report said.
The current expenditure declined by 4.3 per cent in 2017 compared to an increase of 1.7 per cent in 2016, while the investment expenditure contracted by 9.3 per cent in 2017 in comparison with 11.7 percent drop in 2016. Total expenditure as a percentage of GDP reduced to 43.9 per cent as compared to 50.2 per cent in 2016.
The higher support to electricity sector and government organizations, increase in investment expenditure for government companies and reintroduction of targeted fuel subsidy led to increase in the participation and other expenses by 6.2 per cent in 2017 compared to a decline of 45.9 per cent in 2016. Overall, the fiscal deficit declined by 29.1 per cent to RO 3,760 million in 2017 from RO 5,300 million during 2016.
The fiscal balance as a percentage of GDP improved to -13.5 per cent in 2017 as compared to -20.6 percent in 2016. "The fiscal consolidation plan intends to streamline the spending bill and ensure a sustainable fiscal balance through reducing spending deadweight loss and generating revenue from other sources away from oil. At the same time, the growth stimulating government expenditure is desirable to boost diversification in the economy. The government investment expenditure is required to sustain and expand the hydrocarbon production in the economy acting as a catalyst to stimulate private sector-led growth, as the government is the main stakeholder in the oil and gas sector," the apex bank pointed out.
The 2018 budget also continued with reforms agenda and emphasized on diversification, promoting private sector-led growth, and removal of bottlenecks and improving business environment.
The budget stressed upon reducing the breakeven price of oil to ensure a sustainable economy.
The government revenues are budgeted to grow by 11.6 per cent in 2018 over the actual in 2017. The hydrocarbon revenues and non-hydrocarbon revenues are estimated to contribute about 58 per cent and 42 per cent, respectively, to the budgeted increase in government revenue in 2018.
Total expenditure is estimated to go up at a muted rate of 1.8 per cent in 2018 over the actual in 2017, with a growth in investment spending budgeted at 3.4 per cent over the actual in 2017, the report said. Overall, the fiscal deficit is budgeted at RO 3,000 million in 2018, which will be largely financed through debt.
or semi-government companies (manufacturing, petrochemical, and electricity industries) to elevate participation of the private sector in the economy and also contribute to government revenues. Other policy reforms that the government undertook include the deregulation of fuel prices, reduction of electricity subsidy, restructuring the capital increasing private sector partnership, and changing the composition between current and capital expenditures," it added.


text:
Revolver – album The Beatles
 Revolver – film
 Revolver – magazyn muzyczny
 Revolver – gitara elektryczna Silvertone
 Revolver – drugi singel Madonny z płyty Celebration

Zobacz też:
 Rewolwer – broń palna

redpajama_set_name: RedPajamaWikipedia


Time Agency is now on audiobook. The production value of this one is an experience in itself. For anyone who has listened to any radio theatre, such as War of the Worlds, you'll really like this one. In addition to crisp narration from Michael Burnette, there are sound effects, ambient noises, and all sorts of extras that made me almost forget that I was listening to a book. You don't want to miss this one and can check it out here.
For all of those who've asked about sequels, for this book and for others. Yes, there are planned sequels for most of what I write. As far as the timing on the sequels (ie when you can read them), I can't say. Right now, I write in the gaps of the day when I get a chance while working full time in addition to spending time with my family. So sequels are forthcoming, I can't tell you when.
Finally, last and not very least. Othello and Zombies still needs nominations. If you have any friends or family that would like to get in on it. Now's the time. The polls are closing. Oct. 8th to be precise.


Two reviews close out the month of August here at SuperPhillip Central. The first review is tonight with this in-depth review of Mega Man Legacy Collection 2. Here it is for all readers and passersby to enjoy!
Despite including six full games in its previous collection of Mega Man games, this time around, Capcom merely offers four games within its newly released Mega Man Legacy Collection 2, including Mega Man 7, Mega Man 8, Mega Man 9, and Mega Man 10 (the latter two games featuring all of the downloadable content from their original releases last generation). What -- you can't even include Mega Man & Bass as a bonus here, Capcom?! ...Calming down here, also featured in the collection are art galleries, sound tests, and extra challenges separate from the game menus, offering stage remixes, boss rushes, and Buster-only runs for earning medals and comparing your best times with the world via leaderboard.
Unfortunately, what Mega Man Legacy Collection 2 lacks even from its predecessor's catalog of games is some tools that would help modern gamers that aren't so skilled or hardwired in playing these deviously challenging games (well, not so much Mega Man 8). The original Mega Man Legacy Collection offered save states where you could save your progress literally anywhere, so if you died, you could just load up your save from its state and return playing from there. Instead, what is offer is much more limited, such as being able to return to a handful of specific points in levels and something titled the "Legacy Mode" which cuts down how much health you lose when you take damage. These replacements are nowhere near as helpful or desirable as the previous collection's options. They become even more disappointing omissions when you have certain games in Mega Man Legacy Collection 2 that supply you with an extremely limited number of checkpoints, where losing all your lives means starting fresh in the level. Most seasoned players will not have a problem with this, but it will no doubt be a turn off for those lacking as much skill as needed for these games.
As for the games themselves within the collection, let's start off with the earliest game in Mega Man Legacy Collection 2's roster of games. Heck, it only makes sense, right? Mega Man 7 was the classic Mega Man's only mainline entry on the 16-bit Super Nintendo -- so not counting the Japanese-exclusive Rockman & Forte. Mega Man 7 isn't a widely regarded game due to how different it plays and feels. For instance, the sprites are much larger, and the screen space is much more limited compared to the Mega Man games before it.
In Mega Man 7, Dr. Wily is back to being back to being back (etc.) to no good!
Regardless, Mega Man 7 has a lot going for it. The colorful visual style of the series up to that point had never looked better and more vibrant, and the upgrade in hardware allowed for more awesome visuals and graphical effects. Mega Man 7 is the first Mega Man game to have a regular requirement of returning to levels to discover new power-ups. Mega Man 5 had the letters, yes, but you could get those on the first try through its levels.
Moving on to my favorite game in the collection, Mega Man 8, which saw the Mega Man series as a whole move from Nintendo systems to Sony and Sega platforms due to Nintendo's decision to stick with cartridges for its Nintendo 64, a decision that would attract other third party developers over to other platforms.
Of course, beauty isn't everything, but I still love Mega Man 8 otherwise regardless.
Mega Man 8 is partly my favorite due to its general easiness in difficulty, while offering a good level of challenge, and partly due to nostalgia. It was the first classic series Mega Man game I had ever beat. Despite these things, other notable features that I really get enthused about Mega Man 8 is the vibrant and colorful art style the game uses, the woeful but so-bad-it's-good voice acting along with its better production values that the jump to the CD format assisted with greatly, and the game having the most longevity of any mainline game. I'm, of course, talking about hidden bolts in each level, in many of which you need to return to previously beaten levels to reach or find them with new Robot Master weapons acquired. The Robot Master weapons aren't just tools of destruction, but many could be used as means to reach and access new areas of level.
If you look to the sides, you'll notice an alternate border you can choose while playing Mega Man 8.
Sadly, Mega Man Legacy Collection 2 uses the PlayStation build of Mega Man 8 rather than the more impressive and rare Sega Saturn build. Not only did that version feature better visuals and sound, but also extra content in the form of the return of two Robot Masters, Cut Man and Wood Man.
Then, comes Mega Man 9 which unlike 8 before it has a much stiffer, brutal challenge to it compared to every other game in this collection. That's just one of the reasons why it's close to my least favorite mainline Mega Man game. From its "Gotcha! You're dead!" cheap moment and its fetish to make the game harder by throwing a cavalcade of spikes anywhere and everywhere in the game's levels, Mega Man 9 is not enjoyable for me, personally, to play. Cheap deaths don't result in fun; they result in an artificial difficulty, especially when you're forced to play long sections of level all over again just to get to the hard part at the end of that section and die from touching an awkwardly placed spike trap.
This Robot Master certainly brought the heat to this battle.
On the good side, Mega Man 9's selection of weapons is one of the series' best with pretty much every one of them having a beneficial use to take out foes instead of just having usefulness against a particular Robot Master. Mega Man 9's in-game shop also eases the burden with the ability to purchase (with the bolt currency found in the game) helpful items like extra lives and energy tanks to make it easier to endure the more sinister designed sections of levels. It decided to forgo the current path of modern Mega Man games by going completely retro, back to its roots in all of its NES-styled glory.
Finally, there's Mega Man 10, which released shortly after Mega Man 9 -- perhaps too shortly as it tried to jump on the popularity of Mega Man 9 but felt like more of the same. Mega Man 10 does offer an easy mode unlike Mega Man 9, but this feels too insulting as it covers many spike traps and other dangerous jumps, making once challenging levels completely lacking in any difficulty. Still, the general difficulty of Mega Man 10 is ratcheted down compared to 9, and it's a more welcoming game for that. The addition of playing as Bass brings more replay value to Mega Man 10 as well.
Mega Man Legacy Collection 2 brings the player many choices in how the various games are displayed and how they can be controlled. Each Mega Man game here can be played in its normal resolution, a more stretched out format, and formats that put borders around the screen space, all themed after the current Mega Man game being played (there's a selection to choose from). The button configurations for each game are available to be customized. For example, I put the dash button to Circle and the special weapon button to Triangle in Mega Man 8, just the way I used to play it in middle school. So, overall, there is an admirable range of playing each game in the collection the way you desire whether with presentation in mind or with regards to controls.
Not only can you adjust the border, but you can also adjust the screen size for each game.
While Mega Man Legacy Collection 2 may remove its migraine-preventing "save whenever you want" feature from the previous Legacy Collection and add a undesirable substitute instead, I can't really shame this second compendium of Mega Man titles down too much. It's but twenty dollars, offers four excellent Mega Man games, plenty of bonuses, and is just a good deal in general. If you're easy to get irritated, then maybe just stick with the original Mega Man Legacy Collection with its "save anywhere" functionality. For everyone else, Mega Man Legacy Collection 2 is worth it to see how far the series evolved from its NES days... and sort of de-made itself back into its NES days soon after.


I have been working on the garden, but honestly, there is too much to do. Many plants have been growing and there is plenty of greenery. Most of the early Irises have bloomed, which has been enjoyable. The VegTrug has proven to be wonderful and I have transplanted the broccoli, basil and tomatoes directly into the ground now that they have filled out nicely.
Recently I added more reed fencing. This time to the backside of the neighbors fence that runs behind the cottage. I also created a closed off area behind the cottage to hide the bins and extra containers and pots. I think it turned out nicely.
A new utility area behind the cottage.
We also extended a bed near the cottage and added plants such as Ruellia that I brought back from the last trip to Florida. It's nice because Ruellia here in eastern North Carolina is only just coming up so I have a nice clump for the tour. As well, I added some phlox that came from our other neighbors yard that had spread to our side of the fence. I had also broke up a clump of Canna lilies from the kitchen garden and added this to the bed as well. I believe by summer there should be a nice cluster of flowers of varying heights.
It was time to remove the ornamental kale and pansy's from the large pot in the front yard. For a showy effect I planted Cordyline, Croton, Cuphea hyssopifolia, Ipomoea batatas and Setcreasea Purpurea. I believe all these plants will fill out nicely with the height of the Cordyline and Croton to the tiny pink flowers of the Mexican Heather and trailing lime-green Sweet potato vine and the deep purple foliage and pink flowers of the trailing Purple heart.
My supervisor at work gave me three dwarf water lilies called "Nymphaea Helvola." Currently, they are in a container that is too small and they are beginning to reach the top of the water. My roommate and I recently found a blue glazed terracotta pot that will be used for the water lilies, but the drainage hole on the bottom needed to be sealed up. I did this with a wine cork and silicone and now we wait for this to dry.
I am looking forward to the added element to the garden.
Eventually they should look like this picture.
Rain had moved in and stayed for three days straight. The paths and beds have turned into ponds and streams. Sigh. Some major projects such as painting the cottage and putting up the arbor and picket fence has not happened. I am not so sure either one will be done in time for the garden tour. I am trying to maintain composure and find my Zen, but this isn't always easy for someone who thinks too much on random things like, "Oh my gods, I'm going to be 60 in ten years!" or "Ugh, I have 15 years until I retire!" You know, things like that. Anyhoo… At this point I simply have to channel Doris Day.
← Cottage Preparations and Spring has Sprung (again)!


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Rhode Island Federal District Court Denies Motion To Assert Third-Party Claim As Untimely
| Dec 13, 2013 | Firm News
Rhode Island's federal district court has denied plaintiff's motion filed after the close of fact discovery to assert a third-party claim with respect to defendant's counterclaim. The court held the motion was untimely and the proposed third-party complaint deficient in that it was unclear how the proposed claims related to the counterclaim.
Plaintiff filed suit alleging breach of warranty claims respecting "equipment" made by defendants and sold to plaintiff. One defendant counterclaimed alleging plaintiff had failed to pay for other goods. Plaintiff denied failure to pay and essentially asserted its breach of warranty claims as an offset. The parties initiated discovery and settlement negotiations. The latter ultimately resulted in the dismissal of plaintiff's claims. Two weeks after fact discovery closed, plaintiff filed its motion to assert a third-party claim respecting the counterclaim.
The court said the counterclaim was relatively straightforward assertion that defendant had sold and delivered goods to plaintiff and that plaintiff had not paid for them. The proposed third-party complaint alleged that third-party defendant induced plaintiff to purchase goods made by the third-party defendant and that the third-party defendant had breached various warranties, etc., with respect to those goods.
The court held that the motion to assert the third-party complaint was untimely because it was filed after the close of fact discovery. Moreover, whether the third-party claims relate to the counterclaim was "oblique at best." There is no explicit connection stated between the goods for which defendant seeks payment and the goods identified in the third-party complaint. The court said it might have given plaintiff an opportunity to amend the proposed third-party complaint except that it had been served so late. Allowing the third-party claim would unduly delay defendant's relatively simple claim. Accordingly, the motion was denied.
Gogocast, Inc. v. LG Electronics U.S.A., Inc., C.A. No. 12-524ML, slip opinion (D.R.I. Nov. 27, 2013)
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WINTER CAR CARE IS ABOUT PREVENTION!
FALL & WINTER PREP IS HERE! Get your auto, truck, van in shape to protect your vehicle against the winter elements.
Winterizing your car, vehicle, truck, fleet inventory, motorcycle, and any other motorized transportation is essential to the value and longevity of your vehicle. Protecting the exterior with professional detailing helps with the wear and tear from snow, sleet, ice, rain, mud, sand and frigid temperatures. From the cold air drying the rubber of your tires by making them stiff, to the abrasive salt, which is notorious for prematurely degrading and damaging waxes and sealants thereby creating exterior rust in addition to salt stains on interior carpets and upholstery.
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Awesome Without Borders создатель проекта Alexander Blake
Tonality is a new professional ensemble in Los Angeles. Formed a little more than a year ago, this new choral group was created to presents shows based on diversity, peace, and social justice. The basis of the choir was an increasing sense of awareness of our inability to see each other as humans trying to exist in one space. Whether it is race, religion, gender identity, sexual orientation, or any other characteristics we use to distinguish groups, we have found that these identities have divided us. Through the programming of Tonality, our mission is to use the music to increase sensitivity and a greater sense of community.
This year, Tonality has programmed different topics that we felt were very relevant in today's social and political environment. The first concert, titled "Put Your Guns Down", was a concert that dealt with the different themes related to gun violence. These included mass shooting, individual tragedies and living victims, and how we can all find an active way of peace to help combat the issues. Our holiday concert, "Stories of Home", used the original Nativity story to relate Mary and Joseph to issues of refugees, immigration, and homelessness. Our concert on gun violence will be repeated in January.
With each of these concerts, we have either singers in the group or different community members give personal stories related to these topics in order to make the subjects more personal and to put a face to the issues. These stories help to bring the issues closer to the community. Furthermore, we invite organizations to be present at our concert so our audience members can discover ways to get involved in the issues presented in real time.
Our last concert that we will present this season is called "Follow Her Lead", and it will be focusing on women's rights issues.
Грант предоставил Awesome Without Borders (February 2018)


Planet Garth Forums > Main > Planet Garth Forum > New Garth Record...well sort of.
View Full Version : New Garth Record...well sort of.
14.Its Midnight Cinderella (from the Jay Leno show 1995) I made an MP3 of this song and it sounds so good that I used it as a bonus track. I wanted a version of a Garth tune that hasn't been released before.


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Châteauredon è un comune francese di 94 abitanti situato nel dipartimento delle Alpi dell'Alta Provenza della regione della Provenza-Alpi-Costa Azzurra.

Società

Evoluzione demografica

Note

Altri progetti

Chateauredon


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Victorius – Space Ninjas from Hell
March 3, 2020 · by stephenskostas · in Album Reviews. ·
A couple years ago, Victorius blew all of our minds with the excellent, over-the-top Dinosaur Warfare, an EP filled to the brim with epic stories of a sci-fi apocalypse featuring cybernetically-augmented dinosaurs battling aliens. This year, they released Space Ninjas from Hell, which is filled to the brim with epic stories of a sci-fi apocalypse featuring cybernetically-augmented space ninjas battling… let me check… at least one cybernetically-augmented samurai and also an "Evil Wizard WuShu Master"… among other things.
SNfH – much like its predecessor – doesn't so much tell a coherent story as it introduces you to a number of characters and then just lets your imagination run wild with them. There is a bit of exposition which falls at the end of the album, but it mostly felt like it belonged at the beginning of the album rather than the end– at the very least to let us know who to cheer for – apparently the Space Ninjas are BAD and the supposedly "Evil Wizard WuShu Master" is actually a good guy? I still don't know how I feel about the "Astral Assassin Shark." Either way the whole thing is a lot of fun, but is it more than just fun?
Short answer: No. It's mostly just fun, without a lot to back it up. It starts strong, "Tales of the Sunbladers," "Ninjas Unite," and "Super Sonic Samurai" are three of the best tracks on the album, and they're right at the front. "Shuriken Showdown" is also an amazing pick in the "somewhere between Dragonforce and Gloryhammer" metal genre. But a lot of the tracks just feel silly and maybe a little rushed. "Nippon Knights" and "Wasabi Warmachine" also felt a bit like overdone cliches of Western people referencing Japanese culture.
Fans of power metal looking for something a little bit different, or fans of Gloryhammer who need a little something to tie them over in between tales of Angus McFife will certainly find something to enjoy; but, overall, I think this would have done better as another EP. Twenty minutes of dinosaur power metal was great; but, after 45 minutes, the space ninjas overstayed their welcome.
Find Victorius online:
Tags: metal, Space Ninjas from Hell, Victorius
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Jonathan Hultén – "Chants from Another Place" →
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Apple Becomes World's First-Ever $3 Trillion Company
Company shares temporarily hit a new record of $182.88 on Monday, which secured the valuation before dropping back down.
By Emily Rella January 4, 2022
As a company that has innovated and dominated the technology space for decades, it's no surprise that Apple has broken a seemingly impossible record when it comes to company valuation.
Bloomberg | Getty Images
Apple has officially reached a $3 trillion market valuation, making it the first company to accomplish such a feat.
Related: Apple Is Delaying Its Return-to-Office Date Indefinitely and Giving All Employees a $1,000 Bonus
Apple's Q4 earnings from 2021 were an impressive $83.4 billion, a 29% increase year over year in the same time period.
"We are infusing our values into everything we make — moving closer to our 2030 goal of being carbon neutral up and down our supply chain and across the lifecycle of our products, and ever advancing our mission to build a more equitable future," CEO Tim Cook said in a statement at the time.
Apple most recently surpassed the $2 trillion club in August of 2020.
The company managed to survive and thrive amid the pandemic — during which many other companies faltered and fumbled — thanks to a higher demand for at-home electronic products and high app usage due to lockdowns.
Most recently, Apple began to shutter most stores in New York amid the rise of the Omicron variant and new social distancing guidelines.
As of Tuesday morning, the company was up a whopping 40.65% year over year.
Emily Rella is a news writer at Entrepreneur.com. Previously, she was an editor at Verizon Media, covering entertainment, pop culture, lifestyle, entrepreneuership and business. She is a 2015 graduate of Boston College and a Ridgefield, CT native.
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From TV Shows to NFTs, Content Needs Are Exploding. Here's How Creators Can Have Their Best Year Yet.
Wendy's Roasts Jack in the Box by Making Fun of McDonald's: 'Somehow Not the Worst'
Anton Liaskovskyi


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Q: Как зациклить определенную строчку кода в Java Во время изучения java по книге написал код и хотел бы зациклить лишь строчку, которая выводит текст пользователю на экран
class PhraseGeneration{
public static void main(String[] args) {
    String[] wordListOne = {"small", "big", "enormous", "giant"};
    String[] wordListTwo = {"green", "red", "blue", "pink"};
    String[] wordListThree = {"umbrella", "doll", "coat", "building"};
    int oneLength = wordListOne.length;
    int twoLength = wordListTwo.length;
    int threeLength = wordListThree.length;
     int rand1 = (int)(Math.random() * oneLength);
     int rand2 = (int)(Math.random() * twoLength);
     int rand3 = (int)(Math.random() * threeLength);
      String pharse = wordListOne [rand1] + " " + wordListTwo[rand2] + " " + wordListThree[rand3];
        while (true)
          System.out.print("I'm really dissapointed by that code " + pharse);
}

}
ниже пример, как я пытался вывести именно эту строку в цикл, так же я пробовал перенести out.print в отдельные фигурные скобки, но в этом случае программа не могла найти int pharse
Элемент списка

A: Судя по всему вы хотите в цикле генерировать фразы:
while (true) {
    int rand1 = (int)(Math.random() * oneLength);
    int rand2 = (int)(Math.random() * twoLength);
    int rand3 = (int)(Math.random() * threeLength);
    String pharse = wordListOne [rand1] + " " + wordListTwo[rand2] + " " + wordListThree[rand3];

    System.out.print("I'm really dissapointed by that code " + pharse);
}

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BDC reticle necessary for .300 BLK?
Thread: BDC reticle necessary for .300 BLK?
I have an 8.5 inch .300 BLK pistol. I am considering an optic. I plan to use both supers and subs, suppressed and unsuppressed. Does it make sense to get an optic with some sort of .300 BLK BDC reticle, or just stick to a good non-compensated reticle?
I will almost never shoot this over 200 yards, but my eyes are getting older and am thinking about a LPVO of some sort, or I could do a red dot with a magnifier.
ACOG with Horseshoe works great for me...map the drop and your gtg.
My two Blackouts are both sighted in at 50 yards with my supersonic handloads. The impact points at 50 and 100 yards are the same for both. At 50 yards the bullet is rising into its arc passing through the bullseye and at 100 it is falling through the bull. Where the BDC reticle on the scopes comes into play is for short range shots, or beyond 100 yards. With the center crosshair sighted for 50 yards, I use the second cross below center for 25. That is if your worried about dead center accuracy. An inch or 1 1/2" low from aiming point at 25 yards is still a dead deer.
It could get complicated mixing it all up that way with suppressed/unsuppressed subs and supers. That being said, for that purpose above, I say it is necessary. You have Eotechs, Trijicon and Sig Sauer to choose from just to name a few. It sounds like although you don't plan on going 200 yards over, you still wanna fire supers. With subs and an 8.5 barrel I think you'll be just fine with subsonics hitting targets with that distance.
I'm running PA's Gen ll 1-6 with the ACSS reticle for 300 BLK/7.62 Russian (yeah, I know). Glass isn't the greatest, but it's actually a decent scope for the modest cost.
With Supers it's "Minute of coffee saucer"-regarding the ranging ticks, which works for my purpose of deer hunting to 200 yds.
Guess I look at the 300 BLK as a novelty hunting cartridge, but I think it a very effective CQB round.
1 x 4 (or 6). Zero for whatever config you use most. Get out there with a data book and chart where other POI are with the other configurations are at distances you will commonly encounter. Transfer to dope card. Tape to stock and practice. Enjoy!


Selenium is a trace mineral and an important part of the enzyme glutathione peroxidase. It is an effective antioxidant, especially when combined with vitamin E. It helps prevent harmful chemical reactions from occurring in the body's cells. Protected cells are better able to fight off diseases, including heart disease, cancer, and disorders associated with aging.
Viruses of all kinds the world over, including AIDS, are mutating, growing more resistant to the drugs meant to combat them. Research shows that malnutrition- in particular, deficiencies of selenium and vitamin E- promote such mutation.
Cancer: Acts as an antioxidant, protecting against or reducing the incidence of breast, colon, liver, skin, and respiratory tumors and cancer.
Heart disease: Prevents and manages coronary heart disease, cardiovascular disease, and stroke by helping to lower LDL cholesterol and reduce platelet aggregation. Reduces post-heart attack mortality. Successful in treatment of Keshan disease.
Immunodepression: Boosts immune function and white blood cell development. Suggested in the treatment of depressed immune disorders such as lupus. Contributes to body's ability to fight bactericidal action of phagocytes. Counters heavy metal toxicity such as lead, mercury, and cadmium poisoning. Stimulates antibody formation in response to vaccinations.
Liver disease: Effective against alcohol-induced cirrhosis of the liver and alcoholic cardiomyopathy. Promotes proper liver and metabolic function.
Skin disorders: Indicated for treatment of acne, eczema, psoriasis, vasculitis, and other skin disorders. Responds to vitamin E deficiencies in combination with vitamin E supplementation. Prevents premature aging.
Muscular and inflammatory illness: Effective in treating all major symptoms of myotonic dystrophy. Suggested in the treatment of inflammatory conditions such as rheumatoid arthritis.
Eyesight: Required for the antioxidant protection of the lens; helps prevent cataract formation.
Reproductive health: May assist in male fertility, prostate function, and sperm motility.
Promotes healthy fetal development and may help in prevention of SIDS.
Brewer's yeast and wheat germ, liver, butter, fish and shellfish, poultry, garlic, grains, sunflower seeds, and brazil nuts are all food sources for naturally occurring selenium. The amount of selenium in foodstuffs corresponds directly to selenium levels in soil. The Eastern Coastal Plain and the Pacific Northwest have the lowest soil selenium levels. Herbal sources include alfalfa, burdock root, fennel seed, ginseng, and yarrow.
Selenium is destroyed when foods are refined or processed. A diet consisting of a variety of foods in their original state, not canned, frozen, or commercially prepared is recommended.
People eating good diets and living in or near selenium-rich soils should still consider taking another 50 mcg of selenium daily. Those eating average diets and living in selenium-poor regions should consider taking 100 mcg of selenium daily as a supplement. Those who eat a poor diet or are at risk for cancer and other diseases discussed should consider taking 200 mcg of selenium daily. Cancer patients should take whatever amount of selenium is necessary to restore their blood levels to normal. Children's doses range from 20 to 40mcg depending on age. Pregnant and lactating women's doses are 60 mcg and 70 mcg respectively. Supplementation in children and pregnancy should be on advice of a healthcare provider.
Vitamin E (200 IU -400 IU daily) increases selenium's effectiveness as an antioxidant.
Selenium is usually not toxic. However, high doses (more than 1,000 mcg a day) over time may produce fatigue, arthritis, hair or fingernail loss, garlicky breath or body odor, gastrointestinal disorders, or irritability. High levels of selenium have also been discovered in children with behavioral problems.
Use and absorption of selenium are hampered by high doses of vitamin C, heavy metals and, possibly, high intakes of zinc and other trace minerals. To avoid this, take your vitamin and mineral supplements at different times of the day. All supplements are best absorbed when taken with a meal. Vitamin C may also increase risk of selenium toxicity when high doses of selenium are being administered.
Selenium is essential to life and the diet cannot be relied upon to provide optimal levels of this vital nutrient. Sensible levels of selenium supplementation are the only assurance that you achieve optimal selenium nutrition.


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Q: Having two public IPs on one Amazon (AWS) EC2 server without using AWS VPC? I have a simple AWS EC2 server (Created before Dec 2013). I want to have two domains hosted on it, each with a different public (elastic) IP.
How do I do that?
From what I read, the 'VPC' word keeps appearing, but I don't need any sophisticated VPC - just a simple server listening to two public IPs.
Thanks.

A: Have you created your account after December 4th, 2013?
If so, every EC2 instance you have is already on a VPC - the default VPC.
Each private IP can be mapped to a single public elastic IP. So what you need are more private IPs - and you can do that to either an existing instance or a new instance without touching VPC configuration!
Take a look at this tutorial by AWS.

*

*If you are looking to do this to an instance during setup, ctrl+f for: 'To assign a secondary private IP address when launching an instance in EC2-VPC'

*If you are looking to do this to an existing instance, ctrl+f for: 'To assign a secondary private IP to an existing instance'
Then, on the same tutorial ctrl+f for Associating an Elastic IP Address with the Secondary Private IP Address to see how to add a public IP to your second private IP.
In this tutorial you will see references to EC2-VPC - this means EC2 instances that are on a VPC, and, as I mentioned earlier, if you created your account after December 4th, 2013, it is.
If not, you will need to take a look at how to create a simple VPC and add your instances there.