text:
\section{Introduction}

Deep networks have proven to be highly successful in a wide range of computer vision problems. They not only excel in classification or recognition based tasks, but also deliver comparable performance improvements for complex spatially-structured prediction tasks~\cite{eigen2015predicting} like semantic segmentation, monocular depth estimation etc. However, generalizability of such models is one of the major concerns before deploying them in a target environment, since such models exhibit alarming dataset or domain bias~\cite{chen2017no,kundu2019unsupervised}.
To effectively address this, researchers have started focusing on unsupervised domain adaptation approaches~\cite{csurka2017domain}.
In a fully-unsupervised setting without target annotations, one of the effective approaches~\cite{ghifary2015domain,tzeng2014deep} is to minimize the domain discrepancy at a latent feature level so that the model extracts domain agnostic and task-specific representations. Although such approaches are very effective in classification or recognition based tasks~\cite{tzeng2017adversarial}, they
yield suboptimal performance for adaptation of fully-convolutional architectures, which is particularly essential for spatial prediction tasks~\cite{zhang2017curriculum}. One of the major issues encountered in such scenarios is attributed to the spatially-structured high-dimensional latent representation in contrast to vectorized form~\cite{kundu2018adadepth}. Moreover, preservation of spatial-regularity, avoiding mode-collapse~\cite{salimans2016improved} becomes a significant challenge while aiming to adapt in a fully unsupervised setting.

\begin{figure}[!tbp]
\centering

	\includegraphics[width=1.0\linewidth]{figures_n/cvpr_fig_1c.pdf}
	\caption{A schematic diagram to understand the implications of \textit{cross-task} distillation. The green arrows show consistency in \textit{cross-task} transfer for a source sample. Whereas, the red and purple arrows show a discrepancy (yellow arrows) in cross-task transfer for a target sample as a result of input domain-shift. \textit{UM-Adapt} aims to minimize this discrepancy as a proxy to achieve adaptation at a spatially-structured common latent representation.\vspace{-5mm}
	}
	\label{fig_flow}
\end{figure}

While we aim towards
human level performance, there is a need to explore scalable learning methods, which can yield generic image representation with improved transferability across both tasks and data domains. Muti-task learning~\cite{misra2016cross,kendall2017multi} is an emerging field of research in this direction, where the objective is to realize a task-agnostic visual representation by jointly training a model on several complementary tasks~\cite{hashimoto2016joint,long2015learning}. In general, such networks are difficult to train as they require explicit attention to balance performance across each individual task.
Also, such approaches only address a single aspect of the final objective (i.e. generalization across tasks) ignoring the other important aspect of generalization across data domains.

In this paper, we focus on multi-task adaptation of spatial prediction tasks, proposing efficient solutions to the specific difficulties discussed above. To effectively deliver optimal performance in both, generalization across tasks and input domain shift, we formulate a multitask adaptation framework called \textit{UM-Adapt}.
To effectively preserve the spatial-regularity information during the unlabelled adversarial adaptation~\cite{tzeng2017adversarial} procedure, we propose two novel regularization techniques. Motivated by the fact that, the output representations share a common spatial-structure with respect to the input image, we first introduce a novel contour-based content regularization procedure. Additionally, we formalize a novel idea of exploiting cross-task coherency as an important cue to further regularize the multi-task adaptation process.

Consider a base-model trained on two different tasks, task-A and task-B. Can we use the supervision of task-A to learn the output representation of task-B and vice versa? Such an approach is feasible, particularly when the tasks in consideration share some common characteristics (i.e. consistency in spatial structure across task outputs). Following this reasoning, we introduce a \textit{cross-task Distillation} module (see Figure~\ref{fig_flow}). The module essentially constitutes of multiple encoder-decoder architectures called \textit{task-transfer} networks, which are trained to get back the representation of a certain task as output from a combination of rest of the tasks as the input. The overarching motivation behind such a framework is to effectively balance performance across all the tasks in consideration, thereby avoiding domination of the easier tasks during training. To intuitively understand the effectiveness of \textit{cross-task distillation}, let us consider a particular training state of the \textit{base-model}, where the performance on task-A is much better than the performance on task-B. Here the \textit{task-transfer} network (which is trained to output the task-B representation using the base-model task-A prediction as input) will yield improved performance on task-B, as a result of the dominant base task-A performance. This results in a clear discrepancy between the \textit{base-task-B} performance and the task-B performance obtained through the \textit{task-transfer} network. We aim to minimize this discrepancy which in turn acts as a regularization encouraging balanced learning across all the tasks.

In single-task domain adaptation approaches~\cite{tzeng2017adversarial,kundu2018adadepth}, it is common to employ an ad-hoc discriminator to minimize the domain discrepancy. However, in presence of \textit{cross-task distillation} module, this approach highly complicates the overall training pipeline. Therefore, avoiding such a direction, we propose to design a unified framework to effectively address both the diverse objectives i.e. a) to realize a balanced performance across all the tasks and b) to perform domain adaptation in an unsupervised setting. Taking inspirations from energy-based GAN~\cite{zhao2016energy}, we re-utilize the \textit{task-transfer} networks, treating the \textit{transfer-discrepancies} as output of energy-functions, to adversarially minimize the domain discrepancy in a fully-unsupervised setting.

Our contributions in this paper are as follows:
\begin{itemize}
    \item We propose a simplified, yet effective unsupervised multi-task adaptation framework, utilizing two novel regularization strategies; a) Contour-based content regularization (CCR) and b) exploitation of inter-task coherency using a \textit{cross-task} distillation module.

    \item Further, we adopt a novel direction by effectively utilizing \textit{cross-task} distillation loss as an energy-function to adversarially minimize the input domain discrepancy in a fully-unsupervised setting.

    \item\textit{UM-Adapt} yields \textit{state-of-the-art} transfer learning {results on ImageNet classification, and comparable performance} on PASCAL VOC 2007 detection task, even with a smaller backbone-net. The resulting semi-supervised framework outperforms the current fully-supervised multi-task learning \textit{state-of-the-art} on both NYUD and Cityscapes dataset
\end{itemize}

\begin{figure*}\includegraphics[scale = 1.2]{figures_n/cvpr_fig_2a.pdf}\centering\caption{An overview of the proposed \textit{UM-Adapt} architecture for multi-task adaptation. The blue and pink background wide-channel indicates data flow for synthetic and natural domain respectively. On the right we show an illustration of the proposed \textit{cross-task} distillation module, which is later utilized as an energy-function for adversarial adaptation (Section~\ref{section:3_3_2}).\vspace{-2mm}}
\label{fig_arch}
\end{figure*}

\section{Related work}

\noindent
\textbf{Domain adaptation.}
Recent adaptation approaches for deep networks focus on minimization of domain discrepancy by optimizing some distance function related to higher order statistical distributions~\cite{csurka2017domain}. Works following adversarial discriminative approaches ~\cite{tzeng2015simultaneous,tzeng2017adversarial,ganin2015unsupervised,ganin2016domain} utilize motivations from Generative Adversarial Networks~\cite{goodfellow2014generative} to bridge the domain-gap.
Recent domain adaptation approaches, particularly targeting spatially-structured prediction tasks, can be broadly divided into two sub branches viz. a) pixel-space adaptation and b) feature space adaptation. In pixel-space adaptation ~\cite{atapour2018real,hoffman2017cycada,bousmalis} the objective is to train a image-translation network~\cite{zhu2017unpaired}, which can transform an image from the target domain to resemble like an image from the source domain.
On the other hand, feature space adaptation approaches focus on minimization of various statistical distance metrics~\cite{gretton2009covariate,long2015learning,sun2016deep,hong2018conditional} at some latent feature-level, mostly in a fully-shared source and target domain parameter setting~\cite{ren2018cross}. However, unshared setups show improved adaptation performance as a result of learning dedicated filter parameters for both the domains in consideration~\cite{tzeng2017adversarial,kundu2018adadepth}. But, a fully unshared setup comes with other difficulties such as \textit{mode-collapse} due to inconsistent output in absence of paired supervision~\cite{isola2016image,zhu2017unpaired}. Therefore, an optimal strategy would be to adapt minimally possible parameters separately for the target domain in a partially-shared architecture setting~\cite{kundu2018adadepth}.

\vspace{1.1mm}
\noindent
\textbf{Multi-task learning.}
Multi-task learning~\cite{caruana1997multitask} has been applied in computer vision literature~\cite{misra2016cross} for quite a long time for a varied set of tasks in consideration~\cite{kruthiventi2016saliency,eigen2015predicting,eigen2014depth,han2017heterogeneous,ranjan2017hyperface}. To realize this, a trivial direction is to formulate a multi-task loss function, which weighs the relative contribution of each task, enabling equal importance to individual task performance.
It is desirable to formulate techniques which adaptively modify the relative weighting of individual tasks as a function of the current learning state or iteration. Kendall~\etal~\cite{kendall2017multi} proposed a principled approach by utilizing a joint likelihood formulation to derive task weights based on the intrinsic uncertainty in individual tasks. Chen~\etal~\cite{Chen_2018} proposed a gradient normalization (GradNorm) algorithm that automatically balances training in deep multitask models by dynamically tuning gradient magnitudes. Another set of work, focuses on learning task-agnostic generalized visual representations utilizing the advances in multi-task learning techniques~\cite{yao2012describing,ren2018cross}.

\section{Approach}
\label{section:3}
Here we define the notations and problem setting for unsupervised multi-task adaptation. Consider source input image samples $x_s \in X_s$ with the corresponding outputs for different tasks being $y_s^{depth} \in Y_s^{depth}$, $y_s^{seg} \in Y_s^{seg}$ and $y_s^{normal} \in Y_s^{normal}$ for a set of three complementary tasks namely, monocular-depth, semantic-segmentation, and surface-normal respectively. We have full access to the source image and output pair as it is extracted from synthetic graphical environment. The objective of \textit{UM-Adapt} is
to estimate the most reliable task-based predictions for an unknown target domain input,  $x_t \in X_t$. Considering natural images as samples from the target domain $P(X_t)$, to emulate an unsupervised setting, we restrict access to the corresponding task-specific outputs viz.  $y_t^{depth} \in Y_t^{depth}$, $y_t^{seg} \in Y_t^{seg}$ and $y_t^{normal} \in Y_t^{normal}$. Note that the objective can be readily extended to a semi-supervised setting, considering availability of output annotations for only few input samples from the target domain.

\subsection{UM-Adapt architecture}
\label{section:3_1}
As shown in Figure \ref{fig_arch}, the base multi-task adaptation architecture is motivated from the standard CNN encoder-decoder framework. The mapping function from the source domain, $X_s$ to a spatially-structured latent representation is denoted as $M_s(X_s)$. Following this, three different decoders with up-convolutional layers~\cite{laina2016deeper} are employed for the three tasks in consideration, (see Figure~\ref{fig_arch}) i.e., $\hat{Y}_s^{depth} = C_{depth}\circ M_s(X_s)$,  $\hat{Y}_s^{seg} = C_{seg}\circ M_s(X_s)$, and  $\hat{Y}_s^{normal} = C_{normal}\circ M_s(X_s)$. Initially the entire architecture is trained with full supervision on source domain data. To effectively balance performance across all the tasks, we introduce the \textit{cross-task Distillation} module as follows.

\subsubsection{Cross-task Distillation module}
\label{section:3_1_1}

This module aims to get back the representation of a certain task through a transfer function which takes a combined representation of all other tasks as input. Consider $T$ as the set of all tasks, i.e. $T=\{t_1,t_2,...,t_k\}$, where $k$ is the total number of tasks in consideration. For a particular task $t_i$, we denote $\hat{Y}_s^{t_i}$ as the prediction of the \textit{base-model} at the $t_i$ output-head. We denote the task specific loss function as $\mathcal{L}^{t_i}(.,.)$ in further sections of the paper. Here, \textit{task-transfer} network is represented as $N^{\rightarrow{t_i}}$, which takes a combined set $O_s^{t_i}=\{ Y_s^{t_j} \;\vert\; t_j\in T-\{t_i\}\}$ as the input representation and the corresponding output is denoted as ${Y}_s^{\rightarrow{t_i}} = N^{\rightarrow{t_i}}(O_s^{t_i})$. The parameters of $N^{\rightarrow{t_i}}$, $\theta^s_{N^{\rightarrow{t_i}}}$ are obtained by optimizing a task-transfer loss function, denoted by $\mathcal{L}^{t_i}({Y}_s^{\rightarrow{t_i}}, Y_s^{t_i})$ and are kept frozen in further stages of training. However, one can feed the predictions of \textit{base-model} through the \textit{task-transfer} network to realize another estimate of task $t_i$ represented as $\hat{Y}_s^{\rightarrow{t_i}} = N^{\rightarrow{t_i}}(\hat{O}_s^{t_i})$, where $\hat{O}_s^{t_i}=\{ \hat{Y}_s^{t_j}\; \vert \;t_j\in T - \{t_i\}\}$. Following this, we define the \textit{distillation-loss} for task $t_i$ as $\mathcal{L}^{t_i}(\hat{Y}_s^{\rightarrow{t_i}}, Y_s^{t_i})$. While optimizing parameters of the \textit{base-model}, this \textit{distillation-loss} is utilized as one of the important loss components to realize an effective balance across all the task objectives (see Algorithm~\ref{algo:1}). Here, we aim to minimize the discrepancy between the direct and indirect prediction (via other tasks) of individual tasks. The proposed learning algorithm does not allow any single task to dominate the training process, since the least performing task will exhibit higher discrepancy and hence will be given more importance in further training iterations.

Compared to the general knowledge-distillation framework~\cite{hinton2015distilling}, one can consider ${Y}_s^{\rightarrow{t_i}} = N^{\rightarrow{t_i}}(O_s^{t_i})$ to be analogous to the output of a teacher network and $\hat{Y}_s^{\rightarrow{t_i}} = N^{\rightarrow{t_i}}(\hat{O}_s^{t_i})$ as the output of a student network. Here the objective is to optimize the parameters of the \textit{base-model} by effectively employing the distillation loss which in turn enforces coherence among the individual task performances.

\begin{figure}[!t]
\includegraphics[width=0.9\linewidth]{figures_n/cvpr_fig_3_edge_b.pdf}
\centering\caption{An overview of the (a) proposed CCR framework with (b) evidence of consistency in contour-map computed on the output-map of each task to that of the input RGB image.\vspace{-2mm}}
\label{fig_trn}
\end{figure}

\subsubsection{Architecture for target domain adaptation}
\label{section:3_1_2}

Following a partially-shared adaptation setup, a separate latent mapping network is introduced specifically for the target domain samples, i.e. $M_t(X_t)$ (see Figure~\ref{fig_arch}). Inline with AdaDepth~\cite{kundu2018adadepth}, we initialize $M_t$ using the pre-trained parameters of the source domain counterpart, $M_s$ (\textit{Resnet-50} encoder), in order to start with a good baseline initialization. Following this, only \textit{Res-5} block (i.e. $\theta_{res5}$) parameters are updated for the additional encoder branch, $M_t$. Note that the learning algorithm for unsupervised adaptation does not update other layers in the task-specific decoders and the initial shared $M_t$ layers till the \textit{Res-4f} block.

\begin{algorithm}[!t]
\label{algo:1}
\SetAlgoLined
        /*Initialization of parameters */\\
$\theta_{base}$: \textit{base-model} parameters $\{\theta_{M_s}, \theta_{T_{t_1}},...,\theta_{T_{t_k}}$\}\\

\For {$m$ iterations}{
 \For {task $t_i$; $i=1,2,...,k$}{

    $\mathcal{L}^{t_i}_{dist.} = \mathcal{L}^{t_i}(\hat{Y}_s^{t_i}, Y_s^{t_i}) + \alpha \mathcal{L}^{t_i}(\hat{Y}_s^{\rightarrow{t_i}}, Y_s^{t_i})$\\
    $\theta_{base}^* := \underset{\theta_{base}}{\textrm{argmin}}\hspace{1mm}\mathcal{L}^{t_i}_{dist.}$\\
 }
 }\vspace{1mm}
\caption{\textit{Base-model} training algorithm on fully-supervised source data with cross-task distillation loss. \vspace{-5mm}}
\end{algorithm}

\subsection{Contour-based Content Regularization (CCR)}\label{section:3_2}
Spatial content inconsistency is a serious problem for unsupervised domain adaptation focused on pixel-wise dense-prediction tasks~\cite{hoffman2017cycada,kundu2018adadepth}. In order to address this,~\cite{kundu2018adadepth} proposes a Feature Consistency Framework (\textit{FCF}), where the authors employ a cyclic feature reconstruction setup to preserve the spatially-structured content such as semantic contours, at the \textit{Res-4f} activation map of the frozen \textit{Resnet-50} (till \textit{Res-4f}) encoder. However, the spatial size of the output activation of \textit{Res-4f} feature (i.e. 20$\times$16) is inefficient to capture relevant spatial regularities required to alleviate the contour alignment problem.

To address the above issue, we propose a novel Contour-based Content Regularization (\textit{CCR}) method. As shown in Figure~\ref{fig_trn}a, we introduce a shallow (4 layer) contour decoder, $C_{contour}$ to reconstruct only the contour-map of the given input image, where the ground-truth is obtained using a standard contour prediction algorithm~\cite{xie15hed}. This content regularization loss (mean-squared loss) is denoted as  $\mathcal{L}_{CCR}$ in further sections of the paper. Assuming that majority of the image based contours align with the contours of task-specific output maps, we argue that the encoded feature (\textit{Res-5c} activation) must retain the contour information during the adversarial training for improved adaptation performance. This clearly makes \textit{CCR} superior over the existing approaches of enforcing image reconstruction based regularization~\cite{atapour2018real,murez2017image,sankaranarayanan2018learning}, by simplifying the additionally introduced decoder architecture devoid of the burden of generating irrelevant color-based appearance. $C_{contour}$ is trained on the fixed output transformation $M_s(X_s)$ and the corresponding ground-truth contour pair, i.e. $\mathcal{L}_{CCR}(\hat{Y}_s^{con.},GT^{con.})$. However, unlike \textit{FCF} regularization~\cite{kundu2018adadepth}, the parameter of $C_{contour}$ is not updated during the adversarial learning, as the expected output contour map is independent of the $M_s$ or $M_t$ transformation. As a result, $\mathcal{L}_{CCR}$ is treated as output of an energy-function, which is later minimized for $M_t(X_t)$ to bridge the discrepancy between the distributions $P(M_s(X_s))$ and $P(M_t(X_t))$ during adaptation, as shown in Algorithm~\ref{algoo:2}.

\subsection{Unsupervised Multi-task adaptation}\label{section:3_3}
In unsupervised adaptation, the overall objective is to minimize the discrepancy between the source and target input distributions. However, minimizing the discrepancy between $P(Y_s^{t_i})$ and $P(\hat{Y}_t^{t_i})$ can possibly overcome the differences between the ground-truth $Y_s^{t_i}$ and prediction $\hat{Y}_s^{t_i}$ better, when compared to matching $P(\hat{Y}_s^{t_i})$ with $P(\hat{Y}_t^{t_i})$ as proposed in some previous approaches~\cite{Tsai_adaptseg_2018}. Aiming towards optimal performance, \textit{UM-Adapt} focuses on matching target prediction with the actual ground-truth map distribution, and the proposed \textit{cross-task distillation} module provides a means to effectively realize such an objective.

\subsubsection{UM-Adapt baseline (\textit{UM-Adapt-B})}\label{section:3_3_1}
Existing literature~\cite{long2016unsupervised,kundu2018adadepth} shows efficacy of simultaneous adaptation at hierarchical feature levels, while minimizing domain discrepancy for multi-layer deep architectures.
Motivated by this, we design a single discriminator which can match the joint distribution of latent representation and the final task-specific structured prediction maps with the corresponding true joint distribution. As shown in Figure~\ref{fig_arch}, the predicted joint distribution denoted by $P( M_t(X_t),\, \hat{Y}_t^{depth},\, \hat{Y}_t^{normal},\, \hat{Y}_t^{seg} )$, is matched with true distribution denoted by $P( M_s(X_s),\, Y_s^{depth},\, Y_s^{normal},\, Y_s^{seg} )$, following the usual adversarial discriminative strategy~\cite{kundu2018adadepth} (see Supplementary for more details).
We will denote this framework as \textit{UM-Adapt-B} in further sections of this paper.

\begin{algorithm}[!t]
\label{algoo:2}
\SetAlgoLined
        /*Initialization of parameters */\\
$\theta_{res5}$: \textit{Res5} parameters of $M_t$ initialized from $M_s$\\
$\theta_{N^{\rightarrow{t_i}}}$: parameters of fully trained $N^{\rightarrow{t_i}}$ (i.e. $\theta^s_{N^{\rightarrow{t_i}}}$)\\ \hspace{10mm} on ground-truth task output-maps, ${Y}_s^{t_i}$\\
\For {$n$ iterations}{
\For {$m$ steps}{
 \For {task $t_i$; $i=1,2,...,k$}{
    /* Update trainable parameters of $M_t$  by minimizing energy of target samples.*/\\
    $\mathcal{L}^{t_i}_G = \mathcal{L}^{t_i}(\hat{Y}_t^{\rightarrow{t_i}}, \hat{Y}_t^{t_i})$\\
    $\theta_{res5}^* := \underset{\theta_{res5}}{\textrm{argmin}}\hspace{1mm} \mathcal{L}^{t_i}_G + \lambda\mathcal{L}_{CCR}(\hat{Y}_t^{con.},GT^{con.})$\\
 }}
 \For {task $t_i$; $i=1,2,...,k$}{
    /* Update the energy function $N^{\rightarrow{t_i}}$ */\\
    $\mathcal{L}^{t_i}_D =  \mathcal{L}^{t_i}(\hat{Y}_s^{\rightarrow{t_i}}, {Y}_s^{t_i}) - \mathcal{L}^{t_i}(\hat{Y}_t^{\rightarrow{t_i}}, \hat{Y}_t^{t_i})$\\
    $\theta_{N^{\rightarrow{t_i}}}^* := \underset{\theta_{N^{\rightarrow{t_i}}}}{\textrm{argmin}}\hspace{1mm}\mathcal{L}^{t_i}_D$\\
 }
 }\vspace{1mm}
\caption{Training algorithm of \textit{UM-Adapt-(Adv.)} utilizing energy-based adversarial \textit{cross-task} distillation. In \textit{UM-Adapt-(noAdv.)} we do not update parameters of the t\textit{ask-transfer} network, i.e. $\theta^*_{N^{\rightarrow{t_i}}} = \theta_{N^{\rightarrow{t_i}}}$ throughout the adaptation procedure (see Section~\ref{section:3_3_2}).
\vspace{-4mm}}
\end{algorithm}

\subsubsection{Adversarial cross-task distillation}\label{section:3_3_2}
Aiming towards formalizing a unified framework to effectively address multi-task adaptation as a whole, we plan to treat the \textit{task-transfer} networks, $N^{\rightarrow{t_i}}$ as energy functions to adversarially minimize the domain discrepancy. Following the analogy of Energy-based GAN~\cite{zhao2016energy}, the \textit{task-transfer} networks are first trained to obtain low-energy for the ground-truth task-based source tuples (i.e. (${O}_s^{t_i}, {Y}_s^{t_i}$)) and high-energy for the similar tuples from the target predictions (i.e. ($\hat{O}_t^{t_i}, \hat{Y}_t^{t_i}$)). This is realized by minimizing $\mathcal{L}_D^{t_i}$ as defined in Algorithm~\ref{algoo:2}. Conversely, the trainable parameters of $M_t$ are updated to assign low energy to the predicted target prediction tuples, as enforced by $\mathcal{L}_G^{t_i}$ (see Algorithm~\ref{algoo:2}). Along with the previously introduced CCR regularization, the final update equation for $\theta_{res5}$ is represented as $\mathcal{L}^{t_i}_G + \lambda\mathcal{L}_{CCR}$. We use different optimizers for energy functions of each task, $t_i$. As a result, $\theta_{res5}$ is optimized to have a balanced performance across all the tasks even in a fully unsupervised setting. We denote this framework as \textit{UM-Adapt-(Adv.)} in further sections of this paper.

Note that the \textit{task-transfer} networks are trained only on ground-truth output-maps under sufficient regularization due to the compressed latent representation, as a result of the encoder-decoder setup.
This enables $N^{\rightarrow{t_i}}$ to learn a better approximation of the intended \textit{cross-task} energy manifold, even in absence of negative examples (target samples)~\cite{zhao2016energy}. This analogy is used in Algorithm~\ref{algo:1} to effectively treat the frozen \textit{task-transfer} network as an energy-function to realize a balanced performance across all the tasks on the fully-supervised source domain samples. Following this, we plan to formulate an ablation of \textit{UM-Adapt}, where we restrain the parameter update of $N^{\rightarrow{t_i}}$ in Algorithm~\ref{algoo:2}. We denote this framework as \textit{UM-Adapt-(noAdv.)} in further sections of this paper. This modification gracefully simplifies the unsupervised adaptation algorithm, as it finally retains only $\theta_{res5}$ as the minimal set of trainable parameters (with frozen parameter of $N^{\rightarrow{t_i}}$ as $\theta^s_{N^{\rightarrow{t_i}}}$).

\section{Experiments}\label{section:4}
To demonstrate effectiveness of the proposed framework, we evaluate on three different publicly available benchmark datasets, separately for indoor and outdoor scenes. Further, in this section, we discuss details of our adaptation setting and analysis of results on standard evaluation metrics for a fair comparison against prior art.

\subsection{Experimental Setting}\label{section:4_1}
We follow the encoder-decoder architecture exactly as proposed by Liana \etal~\cite{laina2016deeper}. The decoder architecture is replicated three times to form $C_{depth}$, $C_{normal}$ and $C_{seg}$ respectively. However, the number of feature maps and nonlinearity for the final task-based prediction layers is adopted according to the standard requirements. We use BerHu loss~\cite{laina2016deeper} as the loss function for the depth estimation task, i.e. $\mathcal{L}^{depth}(.,.)$. Following Eigen \etal~\cite{eigen2015predicting}, an inverse of element-wise dot product on unit normal vectors for each pixel location is consider as the loss function for surface-normal estimation, $\mathcal{L}^{normal}(.,.)$. Similarly for segmentation, i.e $\mathcal{L}^{seg}(.,.)$, classification based cross-entropy loss is implemented with a weighing scheme to balance gradients from different classes depending on their coverage.

We also consider a semi-supervised setting (\textit{UM-Adapt-S}), where the training starts from the initialization of the trained unsupervised version, \textit{UM-Adapt-(Adv.)}. For better generalization, alternate batches of labelled (optimize supervised loss, $\mathcal{L}_{dist.}^{t_i}$) and unlabelled (optimize unsupervised loss, $\mathcal{L}_{G}^{t_i}+\lambda\mathcal{L}_{CCR}$) target samples are used to update the network parameters (i.e. $\theta_{res5}$).

\begin{table}
 \centering
 \caption{Quantitative comparison of different ablations of \textit{UM-Adapt} framework with comparison against prior arts for depth estimation on NYUD-v2. The second column indicates amount of supervised target samples used during training.
 }

\setlength\tabcolsep{3pt}
\resizebox{1.01\linewidth}{!} {
\begin{tabular}{l|c|ccc|ccc}
\hline
\multirow{2}{*}{Method} & \multirow{2}{*}{\makecell{sup.}} & \multicolumn{3}{c|}{\tabincell{c}{Error$\downarrow$}} & \multicolumn{3}{c}{\tabincell{c}{Accuracy $\uparrow$ ($\gamma = 1.25$)}} \\ \cline{3-8}
                                 &     & rel & log10 & rms & $\delta < \gamma$ & $\delta<\gamma^2$ & $\delta < \gamma^3$ \\ \hline \hline

Saxena \etal~\cite{saxena2009make3d}     &    795            & 0.349 &  -     & 1.214  &0.447 & 0.745 & 0.897 \\

Liu \etal~\cite{liu2015deep}         &             795           & 0.230 &0.095& 0.824& 0.614& 0.883&  0.975 \\

Eigen \etal~\cite{eigen2014depth}  &        120K            & 0.215  &  -      & 0.907& 0.611 &  0.887  &  0.971\\
Roy~\etal~\cite{roy2016monocular}         &          795                & 0.187  &  0.078& 0.744& - &  -  &  -\\

Laina \etal~\cite{laina2016deeper}         &     96K       & 0.129  &0.056& {0.583}&0.801 & 0.950 & 0.986\\
 \hline  \multicolumn{8}{c}{Simultaneous multi-task learning}\\  \hline
Multi-task baseline & 0 &0.27 &0.095	&0.862		&0.559	&0.852	&0.942 \\
UM-Adapt-B(FCF) & 0 &0.218	&0.091 &0.679		&0.67	&0.898	&0.974 \\
UM-Adapt-B(CCR) & 0 &0.192 &0.081	&0.754	&0.601	&0.877	&0.971 \\
UM-Adapt-(noAdv.)-1 & 0 &0.181	&0.077 &0.743 &0.623	&0.889	&0.978\\
UM-Adapt-(noAdv.) & 0 &0.178	&\textbf{0.063} &0.712 &{0.781}	&0.917	&0.984\\
UM-Adapt-(Adv.) & 0 &\textbf{0.175} &0.065	&\textbf{0.673}		&\textbf{0.783}	&\textbf{0.92}	&\textbf{0.984}\\ \hline
Wang \etal~\cite{wang2015towards}     &     795         & 0.220 & 0.094 &0.745& 0.605 & 0.890 & 0.970 \\
Eigen \etal~\cite{eigen2015predicting}   &    795        & 0.158  & -       & 0.641& 0.769 & 0.950 & 0.988 \\
Jafari~\etal~\cite{jafari2017analyzing} & 795 & 0.157 & 0.068 & 0.673 & 0.762 & 0.948 & 0.988 \\
UM-Adapt-S & 795 &\textbf{0.149}	&\textbf{0.067} &\textbf{0.637}		&\textbf{0.793}	&\textbf{0.938}	&\textbf{0.983} \\

\hline
\end{tabular}
}
\label{t_nyud_depth}
\vspace{-5pt}
\end{table}

\begin{table}
 \centering
 \caption{Quantitative comparison of different ablations of \textit{UM-Adapt} framework with comparison against prior arts for Surface-normal estimation on the standard test-set of NYUD-v2.
 }

\setlength\tabcolsep{3pt}
\resizebox{1.01\linewidth}{!} {
\begin{tabular}{l|c|cc|ccc}
\hline
\multirow{2}{*}{Method} & \multirow{2}{*}{\makecell{sup.}} & \multicolumn{2}{c|}{\tabincell{c}{Error $\downarrow$}} & \multicolumn{3}{c}{\tabincell{c}{Accuracy$\uparrow$}} \\ \cline{3-7}
                                 &     & mean & median & 11.25\degree  & 22.5\degree & 30\degree \\ \hline \hline
Eigen~\etal~\cite{eigen2015predicting} & 120k & 22.2	&15.3	&38.6	&64	&73.9 \\
PBRS~\cite{zhang2016physically}  & 795 & 21.74	&14.75	&39.37	&66.25	&76.06   \\
SURGE~\cite{wang2015designing} & 795 &20.7 & 12.2 & 47.3 & 68.9 &76.6 \\
GeoNet~\cite{qi2018geonet} &30k &\textbf{19.0}	&\textbf{11.8}	&\textbf{48.4}	&71.5	&79.5 \\
 \hline  \multicolumn{7}{c}{Simultaneous multi-task learning}\\  \hline
Multi Task Baseline&0	&25.8	&18.73	&29.65	&61.69	&69.83 \\
UM-Adapt-B(FCF)&0	&24.6	&16.49	&37.53	&65.73	&75.51 \\
UM-Adapt-B(CCR)&0	&23.8	&14.67	&42.08	&69.13	&77.28 \\
UM-Adapt-(noAdv.)-1&0	&22.3	&15.56	&43.17	&69.11	&78.36 \\
UM-Adapt-(noAdv.)&0	&22.2	&15.31	&\textbf{43.74}	&70.18	&78.83 \\
UM-Adapt-(Adv.)&0	&\textbf{22.2}	&\textbf{15.23}	&43.68	&\textbf{70.45}	&\textbf{78.95} \\
\hline
UM-Adapt-S &795	&21.2 &{13.98}	&44.66	&\textbf{72.11}	&\textbf{81.08} \\ \hline
\end{tabular}
}\label{t_nyud_normal}\vspace{-5pt}
\end{table}

\vspace{1.5mm}
\noindent
\textbf{Datasets.}
For representation learning on indoor scenes, we use the publicly available NYUD-v2~\cite{silberman2012indoor} dataset, which has been used extensively for supervised multi-task prediction of depth-estimation, sematic segmentation and surface-normal estimation. The processed version of the dataset consists of 1449 sample images with a standard split of 795 for training and 654 for testing. While adapting in semi-supervised setting, we use the corresponding ground-truth maps of all the 3 tasks (795 training images) for the supervised loss. The CNN takes an input of size 228$\times$304 with various augmentations of scale and flip following~\cite{eigen2014depth}, and outputs three task specific maps, each of size 128$\times$160. For the synthetic counterpart, we use 100,000 randomly sampled synthetic renders from PBRS~\cite{zhang2016physically} dataset along with the corresponding clean ground-truth maps (for all the three tasks) as the source domain samples.

To demonstrate generalizability of \textit{UM-Adapt}, we consider outdoor-scene dataset for two different tasks, sematic segmentation and depth estimation. For the synthetic source domain, we use the publicly available GTA5~\cite{richter2016playing} dataset consisting of 24966 images with the corresponding depth and segmentation ground-truths. However, for real outdoor scenes, the widely used KITTI dataset does not have semantic labels that are compatible with the synthetic counterpart. On the other hand, the natural image Cityscapes dataset~\cite{cordts2016cityscapes} does not contain ground-truth depth maps. Therefore to formulate a simultaneous multi-task learning problem and to perform a fair comparison against prior art, we consider the Eigen test-split on KITTI~\cite{eigen2015predicting} for comparison of depth-estimation result and the Cityscapes validation set to benchmark our outdoor segmentation results in a single \textit{UM-Adapt} framework.
For the semi-supervised setting, we feed alternate KITTI and Cityscapes minibatches with the corresponding ground-truth maps for supervision. Here, input and output resolution for the network is considered to be 256$\times$512 and 128$\times$256 respectively.

\begin{table}[!t]
 \centering
 \caption{Quantitative comparison of different ablations of \textit{UM-Adapt} framework with comparison against prior arts for sematic segmentation on the standard test-set of NYUD-v2.
 }
\setlength\tabcolsep{3pt}
\resizebox{1.01\linewidth}{!} {
\begin{tabular}{l|c|ccc}
\hline Method & \makecell{Sup.}  & Mean IOU & Mean Accuracy & Pixel Accuracy \\
 \hline \hline
 PBRS~\cite{zhang2016physically}  & 795 & 0.332	& -	&-   \\
Long~\etal~\cite{long2015fully} & 795 & 0.292	&0.422	&0.600 \\
Lin~\etal~\cite{lin2016efficient} &795 &0.406	&0.536	&0.700 \\
Kong~\etal~\cite{kong2017recurrent} &795 &\textbf{0.445} & - & 0.721\\
RefineNet(Res50)~\cite{lin2017refinenet} & 795& 0.438 &- &-\\
 \hline  \multicolumn{5}{c}{Simultaneous multi-task learning}\\  \hline
Multi Task Baseline&0	&0.022	&0.063	&0.067 \\
UM-Adapt-B(FCF)&0	&0.154	&0.295	&0.514 \\
UM-Adapt-B(CCR)&0	&0.163	&0.308	&0.557 \\
UM-Adapt-(noAdv.)-1&0	&0.189	&0.345	&0.603 \\
UM-Adapt-(noAdv.)&0	&0.214	&{0.364}	&0.608 \\
UM-Adapt-(Adv.)&0	&\textbf{0.221}	&\textit{0.366}	&\textbf{0.619} \\
\hline
Eigen~\etal~\cite{eigen2015predicting} &795 &0.341 &0.451 &0.656 \\
Arsalan~\etal~\cite{mousavian2016joint} &795 &0.392 &0.523 &0.686 \\
UM-Adapt-S &795	&\textbf{0.444}	&\textbf{0.536}	&\textbf{0.739} \\

\hline
\end{tabular}
}
\label{t_nyud_segment}
\vspace{-5pt}
\end{table}

\vspace{1.5mm}
\noindent
\textbf{Training details.}
We first train a set of task-transfer networks on the synthetic task label-maps separately for both indoor (PBRS) and outdoor (GTA5) scenes. For indoor dataset, we train only the following two \textit{task-transfer} networks; $N^{\rightarrow{seg}}({Y_s^{depth}, Y_s^{normal}})$ and $N^{\rightarrow{depth}}({Y_s^{seg}})$ considering the fact that surface-normal and depth estimation are more correlated, when compared to other pair of tasks.  Similarly for outdoor, we choose the only two {task-transfer} possible combinations $N^{\rightarrow{seg}}({Y_s^{depth}})$ and $N^{\rightarrow{depth}}({Y_s^{seg}})$. Following this, two separate \textit{base-model}s are trained with full-supervision on synthetic source domain using Algorithm~\ref{algo:1} ($\alpha=10$) with different optimizers (Adam~\cite{kingma2014adam}) for each individual task. After obtaining a frozen fully-trained source-domain network, the $C_{contour}$ network is trained as discussed in Section~\ref{section:3_2} and it remains frozen during its further usage as a regularizer.

\subsection{Evaluation of the \textit{UM-Adapt} Framework}\label{section:4_2}
We have conducted a thorough ablation study to establish effectiveness of different components of the proposed \textit{UM-Adapt} framework. We report results on the standard benchmark metrics as followed in literature for each of the individual tasks, to have a fair comparison against state-of-the-art approaches. Considering the difficulties of simultaneous multi-task learning, we have clearly segregated prior art based on single-task or multi-task optimization approaches in all the tables in this section.

\begin{table}
 \centering
 \caption{Quantitative comparison of ablations of \textit{UM-Adapt} framework with comparison against prior arts for depth-estimation on the Eigen test-split~\cite{eigen2014depth} of KITTI dataset.
 }

\setlength\tabcolsep{3pt}
\resizebox{1.01\linewidth}{!} {
\begin{tabular}{l|c|cccc
\hline
\multirow{2}{*}{Method} & \multirow{2}{*}{\makecell{Target image\\ supervision}} &\multicolumn{4}{c}{\tabincell{c}{Error$\downarrow$}}
\\ \cline{3-6} &     & rel & sq.rel & rms & rms(log10)
                                 \\ \hline \hline

Eigen~\etal~\cite{eigen2014depth}& Full &0.203 &1.548 &6.307 &0.282  \\
Godard~\etal~\cite{godard2017unsupervised} & Binocular &0.148 &1.344 &5.927 &0.247 \\
zhou~\etal~\cite{zhou2017unsupervised} & Video &0.208 &1.768 &6.856 &0.283  \\
AdaDepth~\cite{kundu2018adadepth} &No &0.214 &1.932 &7.157 &0.295  \\
 \hline  \multicolumn{6}{c}{Simultaneous multi-task learning}\\  \hline
Multi-task baseline & No &0.381	&2.08	&8.482	&0.41 \\

UM-Adapt-(noAdv.) & No &0.28	&1.99	&\textbf{7.791}	&0.346\\
UM-Adapt-(Adv.) & No &\textbf{0.27} &\textbf{1.98}	&7.823	&\textbf{0.336}\\ \hline
UM-Adapt-S & few-shot &\textbf{0.201} &\textbf{1.72} &\textbf{5.876} &\textbf{0.259} \\
\hline
\end{tabular}
}
\label{t_kitti_depth}
\vspace{-5pt}
\end{table}

\vspace{1.5mm}
\noindent
\textbf{Ablation study of \textit{UM-Adapt}.}\label{section:4_2_1}\hspace{2mm}
As a multi-task baseline, we report performance on the standard test-set of natural samples
with direct inference on the frozen source-domain parameters without adaptation. With the exception of
results on sematic-segmentation, baseline performance for the other two regression tasks (i.e. depth estimation and surface-normal prediction) are strong enough to support the idea of achieving first-level generalization using multi-task learning. However, the prime focus of \textit{UM-Adapt} is to achieve the second-level of generalization through unsupervised domain adaptation. In this regard, to analyze effectiveness of the proposed \textit{CCR} regularization (Section~\ref{section:3_2}) against FCF~\cite{kundu2018adadepth}, we conduct an experiment on the \textit{UM-Adapt-B} framework defined in Section~\ref{section:3_3_1}. The reported benchmark numbers for unsupervised adaptation from PBRS to NYUD (see Table~\ref{t_nyud_depth},~\ref{t_nyud_normal} and~\ref{t_nyud_segment}) clearly indicates the superiority of \textit{CCR} for adaptation of structured prediction tasks. Following this inference, all later ablations (i.e. \textit{UM-Adapt-(noAdv.)}, \textit{UM-Adapt-(Adv.)} and \textit{UM-Adapt-S}) use only \textit{CCR} as  content-regularizer.

\begin{table}
 \centering
 \caption{Quantitative comparison of ablations of \textit{UM-Adapt} framework with comparison against prior arts for sematic segmentation on the validation set of Cityscapes dataset.
 }
\setlength\tabcolsep{17pt}
\resizebox{1.01\linewidth}{!} {
\begin{tabular}{l|c|c}
\hline Method & \makecell{Image supervision}  & Mean IOU \\   \hline \hline
FCN-Wild~\cite{hoffman2016fcns} & 0 & 0.271 \\
CDA~\cite{zhang2017curriculum} & 0 & 0.289 \\
DC~\cite{tzeng2015simultaneous} & 0 & 0.376 \\
Cycada~\cite{hoffman2017cycada} & 0 & 0.348 \\
AdaptSegNet~\cite{Tsai_adaptseg_2018} & 0 & 0.424 \\
 \hline  \multicolumn{3}{c}{Simultaneous multi-task learning}\\  \hline
Multi Task Baseline &0	&0.224 \\
UM-Adapt-(noAdv.) &0	&0.408 \\
UM-Adapt-(Adv.) &0	&\textbf{0.420} \\
\hline
UM-Adapt-S &500 &\textbf{0.544} \\

\hline
\end{tabular}
}
\label{t_city_seg}
\vspace{-5pt}
\end{table}

Utilizing gradients from the frozen \textit{task-transfer} network yields a clear improvement over \textit{UM-Adapt-B} as shown in Tables~\ref{t_nyud_depth},~\ref{t_nyud_normal} and~\ref{t_nyud_segment} for all the three tasks in NYUD dataset. This highlights significance of the idea to effectively exploit the inter-task correlation information for adaptation of a multi-task learning framework. To quantify the importance of multiple \textit{task-transfer} network against employing a single such network, we designed another ablation setting denoted as \textit{UM-Adapt-(noAdv.)-1}, which utilizes only $N^{\rightarrow{seg}}(Y_s^{depth},Y_s^{normal})$ for adaptation to NYUD, as reported in Tables~\ref{t_nyud_depth},~\ref{t_nyud_normal} and~\ref{t_nyud_segment}.  Next, we report a comparison between the proposed energy-based cross-task distillation frameworks (Section~\ref{section:3_3_2}) i.e. a) \textit{UM-Adapt-(Adv.)} and b) \textit{UM-Adapt-(noAdv.)}. Though, \textit{UM-Adapt-(Adv.)} shows minimal improvement over the other counterpart,  training of \textit{UM-Adapt-(noAdv.)} is found to be significantly stable and faster as it does not include parameter update of the \textit{task-transfer} networks during the adaptation process.

\begin{figure*}\includegraphics[scale = 0.52]
{figures_n/cvpr_fig_nyud.pdf}\centering\caption{Qualitative comparison of different ablations of \textit{UM-Adapt}, i.e. a) Multi-task baseline, b) \textit{UM-Adapt-(Adv.)}, and c) \textit{UM-Adapt-S}.
}\vspace{-1mm}
\label{fig_nyud}\end{figure*}

\vspace{1.5mm}
\noindent
\textbf{Comparison against prior structured-prediction works.}\hspace{2mm}\label{section:4_2_2}
The final unsupervised multi-task adaptation result by the best variant of \textit{UM-Adapt} framework, i.e. \textit{UM-Adapt-(Adv.)} delivers comparable performance against previous fully-supervised approaches (see Table~\ref{t_nyud_depth} and~\ref{t_nyud_segment}).
One must consider the explicit challenges faced by \textit{UM-Adapt} to simultaneously balance performance across multiple tasks in a unified architecture as compared to prior arts focusing on single-task at a time. This clearly demonstrates superiority of the proposed approach towards the final objective of realizing generalization across both tasks and data domains. The semi-supervised variant, \textit{UM-Adapt-S} is able to achieve state-of-the-art multi-task learning performance when compared against other fully supervised approaches as clearly highlighted in Tables~\ref{t_nyud_depth},~\ref{t_nyud_normal} and~\ref{t_nyud_segment}.

Note that the adaptation of depth estimation from KITTI and sematic-segmentation from Cityscapes in a single \textit{UM-Adapt} framework is a much harder task due to the input domain discrepancy (cross-city~\cite{chen2017no}) along with the challenges in simultaneous multi-task optimization setting. Even in such a drastic scenario \textit{UM-Adapt} is able to achieve reasonable performance in both depth estimation and sematic segmentation as compared to other unsupervised single task adaptation approaches reported in Tables~\ref{t_kitti_depth} and~\ref{t_city_seg}.

\vspace{1.5mm}
\noindent
\textbf{Comparison against prior multi-task learning works.}\label{section:4_2_3}\hspace{2mm}
Table~\ref{rt_1} and Table~\ref{rt_2} present a comparison of \textit{UM-Adapt} with recent multi-task learning approaches~\cite{kendall2017multi,Chen_2018} on NYUD test-set and CityScapes validation-set respectively. It clearly highlights ~\textit{state-of-the-art} performance achieved by \textit{UM-Adapt-S} as a result of the proposed \textit{cross-task} distillation framework.

\begin{table}[!h]
 \centering

 \caption{Test error on NYUDv2 with ResNet as the base-mode

 \vspace{0.5mm}}

\setlength\tabcolsep{3pt}
\resizebox{1.01\linewidth}{!} {
\begin{tabular}{l|c|c|c|c
\hline {Method} & {Sup.} &\makecell{Depth rms \\Err. (m)} & \makecell{Seg. Err.\\(100-IoU)} & \makecell{Normals Err.\\(1-$\vert cos\vert$)}\\ \hline

Kendall~\etal~\cite{kendall2017multi}& 30k &0.702 & - &0.182  \\
GradNorm~\cite{Chen_2018} & 30k &0.663 &67.5 &0.155 \\
\textit{UM-Adapt-S} & 795 &\textbf{0.637} &\textbf{55.6} &\textbf{0.139}\\
\hline
\end{tabular}
}
\label{rt_1}
\end{table}

\begin{table}[!h]
 \centering
  \vspace{-3mm}
 \caption{Validation mIOU on Cityscapes, where all the approaches are trained simultaneously for segmentation and depth estimation.

 }

\setlength\tabcolsep{3pt}
\resizebox{1.01\linewidth}{!} {
\begin{tabular}{l|c
\hline {Method} &Semantic (mean IOU)\\ \hline

Kendall~\etal~\cite{kendall2017multi} (Uncert. Weights)& 51.52  \\
Liu~\etal~\cite{liu2018end} & 52.68 \\
\textit{UM-Adapt-S} &\textbf{54.4}\\
\hline
\end{tabular}
}
\label{rt_2}
\vspace{-2mm}
\end{table}

\vspace{1.5mm}
\noindent
\textbf{Transferability of the learned representation.}\label{section:4_2_4}\hspace{2mm}
One of the overarching goals of \textit{UM-Adapt} is to learn general-purpose visual representation, which can demonstrate improved transferability across both tasks and data-domains.
To evaluate this, we perform experiments on large-scale representation learning benchmarks. Following evaluation protocol by Doersch~\etal~\cite{doersch2017multi}, we setup ~\textit{UM-Adapt} for transfer-learning on ImageNet~\cite{russakovsky2015imagenet} classification and PASCAL VOC 2007 Detection tasks. The base trunk till \textit{Res5} block is initialized from our ~\textit{UM-Adapt-(Adv.)} variant (the adaptation of PBRS to NYUD) for both classification and detection task.
For ImageNet classification, we train randomly initialized fully connected layers after the output of \textit{Res5} block. Similarly, for detection we use \textit{Faster-RCNN}~\cite{ren2015faster} with 3 different output heads for object proposal, classification, and localization after the \textit{Res4} block. We finetune all the network weights separately for classification and detection~\cite{doersch2017multi}.
The results in Table~\ref{rt_3} clearly highlight superior transfer learning performance of our learned representation even for novel unseen tasks with a smaller backbone-net.

\begin{table
\centering
 \caption{Transfer learning results on novel unseen tasks.

 }
\centering

\setlength\tabcolsep{3pt}
\resizebox{1.01\linewidth}{!} {
\begin{tabular}{l|c|c|c
\hline {Method} &Backbone &\makecell{Classification\\ImageNet top5} & \makecell{Detection\\PASCAL 2007}\\ \hline

Motion Seg.~\cite{pathak2017learning}&ResNet-101 & 48.29 & 61.13 \\
Exemplar~\cite{doersch2017multi}&ResNet-101 & 53.08 & 60.94 \\
RP+Col+Ex+MS~\cite{doersch2017multi} &ResNet-101 & 69.30 & \textbf{70.53} \\
\textit{UM-Adapt-S} &\textbf{ResNet-50} & \textbf{69.51} & 70.02 \\
\hline
\end{tabular}
}
\label{rt_3}
\vspace{-5pt}
\end{table}

\section{Conclusion}\label{section:5}

The proposed \textit{UM-Adapt} framework addresses two important aspects of generalized feature learning by formulating the problem as a multi-task adaptation approach. While the multi-task training ensures learning of task-agnostic representation, the unsupervised domain adaptation method provides domain agnostic representation projected to a common spatially-structured latent representation. The idea of exploiting cross-task coherence as an important cue for preservation of spatial regularity can be utilized in many other scenarios involving fully-convolutional architectures. Exploitation of auxiliary task setting is another direction that remains to be explored in this context.

\vspace{2mm}
\noindent
\textbf{Acknowledgements.} This work was supported by a CSIR Fellowship (Jogendra) and a grant from RBCCPS, IISc. We also thank Google India for the
travel grant.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


The Majesty 125 embodies the values of the Majesty line of superyachts: luxury that is meaningful, customization without compromise or premium, and performance-based sea keeping. She features five En suite Staterooms that accommodate ten guests and emphasize panoramic views. Outdoor space is highlighted throughout her four decks, with plenty of lounging and entertainment areas fore and aft of the yacht. The distinguished "hawkbill" exterior styling is complimented by careful selection of fine leathers, woods, and marbles to breathe luxury into its exclusive interior design.


Join Southern Gospel mainstays Karen Peck and New River for this eleven track collection of brand new compositions. Includes the hit single "Last Night" plus "Hey," "Whispered Prayers," and more.
Year after year, she's walked across stages accepting Female Vocalist and Soprano Awards...and year after year, they have captured hearts and remained favorites in concert, on radio and in their relationships with longtime fans. Enjoy sixteen of the best songs from one of the most loved Southern Gospel groups around.


Northern-style Curry Noodles: Northern-style Curry Noodles with Accompaniments (Kao Soi). Kasma teaches this dish in Advanced Series Set B (class 1). You might enjoy Kasma's article on Northern-style Curry Noodles).


text:
TheJUMP+
What is TheJUMP+
A JUMP History
Issue 8.2
Le Processus D'ecriture (8.2)
Super Speller (8.2)
It Helps to be in on the Joke (8.2)
After the Glow: Radium Girls (8.2)
Ultra-Ego: Agency in Alter Ego (8.1)
The Impact of Lack of Choice in "My father's long, long legs" (8.1)
Picture Perfect (8.1)
Worth Striving For (8.1)
The Art of Trespassing (8.1)
Identity and Civility on the Internet (7.1)
The Power of Possession (7.1)
How to Feed a Sorority (7.1)
The Adventures of Frank Little (7.1)
Semiotic Domains (a game) (7.1)
Maximus Waste: A Food Waste Mockumentary (6.1)
Plastination! Unlock a World of Post-Mortem Possibility! (6.1)
Through My Own Eyes (6.1)
Remix in Higher Education (5.2)
The Rhetoric of Memes (5.2)
English is for Squares: Thomas Was Alone as a Classroom Text (5.2)
Cyber Bullying (5.2)
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To a Rapper's Delight: An In Depth Look at the Construction of A Musical Collaboration (5.1)
Sounds of Silence (5.1)
Gorgias Revisited (5.1)
Digital Media Proliferation Around the World (4.1)
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Ever After (3.1)
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10 and 2, Are You? (3.1)
Congo Coltan War PSA (2.2)
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Jesse James: From Hero to Villain in the Eyes of One (2.1)
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Response 2 – Maximus Waste: A Food Waste Mockumentary (6.1)
By Kyle Stedman
When I watched Maximus Waste, I was eating soup made of everything that was left in my fridge and freezer: oldish celery, oldish carrots, two half-empty cartons of broth, and five bags of half-eaten frozen veggies, with some lentils tossed in for fun.
This soup made me feel cool when I watched the film. I was able to think, "Oh yeah, I totally identify with The Take-Out Tree Hugger. Seriously, why don't other people have it as together as I do?"
But as I finished my soup and continued watching, I started identifying with other characters, and I didn't feel quite as comfortable with that. For every kettle of leftovers soup I've made, there have been plenty of times I've stepped into the role of The Wretched Waster, tossing out nearly-bad produce just for the space in the fridge. (Sometime in the last year I silently slipped from a bread-heel lover to a bread-heel tosser, something I've never quite admitted to myself until now.) And plenty of times I've sniffed something, been pretty sure it was alright, but then let it go into the trash anyway, just as The Sustenance Stickler would have me do.
Maybe this is a stretch, but I'm reminded of literary theorist Kenneth Burke's theory of rhetoric as identification. It's overly simplistic to boil everything he means by identification down to a single quote, but I'll try anyway:
The key term for the old rhetoric was "persuasion" and its stress was upon deliberate design. The key term for the "new" rhetoric would be "identification," which can include a partially "unconscious" factor in appeal. . . . [I]dentification can also be an end, as when people earnestly yearn to identify themselves with some group or other. Here they are not necessarily being acted upon by a conscious external agent, but may be acting upon themselves to this end. (203)
Yes, this film uses some "deliberate design" in the sense of traditional, persuasive rhetoric: facts are being marshalled by the filmmakers, our emotions are being tugged on, all in purposeful ways. But beyond those carefully crafted rhetorical choices, I feel myself, using Burke's words, "yearn[ing] to identify [myself] with some group or another" in the video; I'm "acting upon [myself]" to discover who I identify with, and what that means.
Of course, that's how films work: we see characters on screen and unconsciously ask ourselves how alike or different we are from them. We put ourselves in their shoes. We remember the time that we, too, tried eating uncooked ramen noodles, and how tempting it is to eat leftover food from other guests' tables at restaurants. (Is that just me?) We identify–and through that identification, we're changed. That's why it's so important that this is a film, not just a website, not just a typed, monomodal (i.e. only using the modality of text) composition. For a complex subject in which all of us share a blend of complicity and concern, we don't just need to be convinced of the topic–we need to be drenched in its complexity, identify with it from multiple angles. That's one reason why the multiple genres of video that the filmmakers included in Maximus Waste make so much sense here (mockumentary, yes, but also formal and informal interviews, public service announcements, and realistic documentary): the more we see the topic from multiple angles, the more we identify, and the more we're changed.
And clearly this topic matters. I mean, it was on NPR the day before I watched the video (Husted), which means these students were months ahead of that national coverage. Yes, that radio story had multiple voices, fleshed out with sound effects and all the affordances of sound. I love sound. But I listened to it it from the corner of my ear, while getting dressed, interested but not feeling like I had to take action or anything. Maximus Waste doesn't leave me that choice. I gave it all my attention (or all the attention I could spare from my soup). I identified. So now I must act.
Burke, Kenneth. "Rhetoric–Old and New." Journal of General Education 5.3 (1951): 202-09. JSTOR. Web. 8 Apr. 2011.
Husted, Kristofor. "Consumers Contribute to Retail Food Waste." NPR. NPR, 19 Nov. 2014. Web. 21 Nov. 2014.
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redpajama_set_name: RedPajamaCommonCrawl


According to the Chester County Daily Local East Marlborough Township Officials have agreed to continue funding the final design of 1.1 miles bike lanes and 2 miles of sidewalk along Route 82 in the Unionville section of the Township.
While an isolated bike lane through a rural township might not seem like a priority, Route 82 hosts a large regional school complex along with several developments on the west side of the road. At the north end of the project the bike lane will terminate at a modern roundabout. Roundabouts are rare in Pennsylvania but these smaller cousins of the NJ traffic circle slow down cars, improve pedestrian safety and produce better traffic flows than signalized intersections. Route 82 may be seen as a model for complete streets in suburban/rural areas.
There are still many hurdles to clear. The region has a huge backlog (>$80 million) of bicycle and pedestrian projects that have been stuck in various stages of development for many reasons. And it remains to be seen if PENNDOT will require a Bicycle Occupancy Permit (BOP), which mandates that municipalities assume liability and maintenance of bike lanes on state roads. Let's keep our fingers crossed and if you live East Marlborough thank your Board of Supervisors for keeping this project alive.


text:
Bootstrap project for developing any kind Chrome extensions.

**Turn this**

manifest.json
```json
{
  "content_scripts": [{
    "js": ["jquery.min.js", "jquery-spellchecker.min.js", "jquery-megamask.min.js", "jquery-scrolltie.min.js", "my-plugins.js", "app.js"],
    "css": ["app.css"]
  }],
}
```

**Into this**

manifest.json
```json
{"content_scripts": [{"js": ["app.js"]}]}
```

Run `npm install jquery jquery-spellchecker jquery-megamask jquery-scrolltie --save`

app.js
```js
import 'jquery'
import 'jquery-spellchecker'
import 'jquery-megamask'
import 'jquery-scrolltie'
import './my-plugins'

import "./app.css"

// Here start my application
$('input').spellchecker()

```

- With all javascript features like [NPM package manager](https://www.npmjs.com/), [Webpack](http://webpack.github.io/), [Babel](https://babeljs.io/), [React](https://facebook.github.io/react/) and almost everything you want.

## Extension features support

- [Browser action](https://developer.chrome.com/extensions/browserAction)
- [Page action](https://developer.chrome.com/extensions/pageAction)
- [Background Pages (Scripts)](https://developer.chrome.com/extensions/background_pages)
  - Support both background scripts or page
- [Content scripts](https://developer.chrome.com/extensions/content_scripts)
  - Supports only scripts. Stylesheets can be easily made and use with webpack
- [Override Pages](https://developer.chrome.com/extensions/override)
  - You can customize **newtab**, **history**, or **bookmarks**

## Installation

1. `npm install`
2. `npm install gulp -g`
  - if there is problem with missing gulpfile, uninstall `npm uninstall gulp -g` and install again. You have probably old non-babel compatible version

## Usage

- Whole repository (package) is simple example extension.
- Check `src/manifest.json` for basic usage of background script, content script and popup.
- Background, content and popup informations are extracted and converted into development environment for extension.
- Ofcourse they can be "compiled" into production extenstion crx file too.
- Your codebase belongs only to `src` directory
- In your scripts you can use **npm packages, babel, react (jsx), styles (with preprocessors) and any modern javascript feature** you know.

## How to run development environment

You should do this before editing any code to see how it works.

1. run `gulp` which will start webpack-dev-server
2. in Chrome open `chrome://extensions/`
3. check `Developer mode`
4. click on `Load unpacked extension`
5. add REPOSITORY_DIRECTORY/build
6. Now you can check background script via link in extension `Inspect views: background page` and you will see some messages in console
7. Navigate to any http or **https** page and open development tools and console. You can see messages from content scripts.
8. Find extension icon (puzzle image) right from adress bar.
  1. Click with left mouse button to show html content
  2. Click with right mouse button and select `Inspect Popup`. Then in console you can see some messages
9. You can edit your codebase with almost 100% hot/full reload support.

## How to build extension

1. run `gulp -p`
2. It will compile scripts, styles and other assets into release/build/
3. It will make chrome extension into release/build.crx with certificate release/build.pem

## TODO

#### Done
- [x] Make manifest.json dynamic based on package.json name, description, version.
- [x] Move everything from build directory and leave it only for building development extenstion.
- [x] Make production build. Replace injector scripts with final builded one.
- [x] Make Hot reload works. There is problem with including hot reload chunks which are included via new script tag into DOM. new code is then eval in default (window) context and not in context of extension. Have to do same hack as you can see in injector files and download chunks via XMLHttpRequest.
- [x] Move `config/manifest.json`, `util/make_injector.js` and `util/make_manifest.js` somewhere out of src to keep src in full will of developer
- [x] Create extension from build process and move it into release/
- [x] Test assets base64 support
- [x] Add asset example. Icon for actions

#### Future

- [ ] Allow to have "static" files which will be merged into build
- [ ] Allow to reload extension when popup html file changed
- [ ] Detect Chrome path for building extension
- [ ] Solve Hot reload fix better than overriding file in /node_modules. It is really ugly and hacky
- [ ] Create example repository with React app
- [ ] Test assets without base64
- [ ] Allow to define entry scripts other way than define them in manifest.json

## Licence

Webpack Chrome Extension is released under the [MIT License](http://www.opensource.org/licenses/MIT).

redpajama_set_name: RedPajamaGithub


Hypersomnolence | Excessive sleepiness, interfering with daily activities.
Hypersomnolence is a condition characterised by excessive sleepiness, which interferes with your daily activities. Patients usually fall asleep during the day at inappropriate times such as during work, while conversing and eating. This condition may also be characterised by symptoms such as difficulty in waking, irritation, hallucinations, loss of appetite, and slow speech and thinking. This condition is mainly caused due to an underlying sleep disorder or neurological condition, sleep deprivation, drug or alcohol abuse, being overweight, head injuries or heredity.


text:
Australia: Catholics vow to go hard in schools fight with govt
Turnbull had unveiled an extra AUD18.6 billion in funding to Australian schools over the next decade. Source: AAP/Lukas Coch/via Reuters
The battle between the Catholics and the government on the schools funding plan appears to be ramping up, with Catholic education authorities intensifying their campaign.
Catholic education sources said a discussion of the way forward was held on Sunday afternoon.
This came after Treasurer Scott Morrison had dug in earlier behind the plan, saying "we're trying to get to a standard at the end of ten years where everybody is on the same wicket".
"There are some schools that are getting more support because of special deals done in the past. Now, there shouldn't be special deals," he told Nine's Laurie Oakes.
"There should be one deal and it should be based on the needs of every single student. That's what we're putting forward."
When it was put to him the Catholic sector was making its case against the government, Morrison said bluntly: "I don't agree with it".
He acknowledged, as Education Minister Simon Birmingham has done, there would be transitional arrangements for those with special circumstances.
But "we can't walk away from the principle" the funding for the schooling of every child "should be based on a clear standard – and it should be fair for everyone".
The government plan embraces the Gonski needs-based approach and commits an extra AUD18.6 billion over a decade across government, Catholic and independent schools.
The Catholic system is getting an overall national increase in funding. But it argues the way in which the parental capacity to contribute is estimated is flawed, to its disadvantage. It is also up in arms about not being consulted before last week's announcement.
The row with the Catholics is flaring just as the government wants Tuesday's budget to act as a "reset" and mollify voters on issues ranging from health to the housing affordability.
The mood of the government backbench to the schools plan will become clearer as Coalition MPs return to Canberra on Monday ahead of Tuesday's party meeting. Tony Abbott has predicted a vigorous debate in the partyroom, but there will also be a lot of pressure not to detract from the budget.
Coalition backbenchers are still getting their heads around the new policy.
One of those seeking more information is outspoken Nationals MP George Christensen. He said on Sunday, by and large, he was supportive of the new funding measures. He had heard some concerns among Catholic schools, but no school in his Queensland regional electorate of Dawson was going backwards. Nevertheless, he would be "reaching out" to local Catholic authorities.
It didn't bother him super elite schools were losing funding, but "if there are losses for Catholic schools and other Christian schools, perhaps we would need to make up for that".
On Friday, the chair of the Bishops Commission for Catholic Education, Archbishop Timothy Costelloe, said the proposal would affect the affordability and accessibility of Catholic schools in a number of jurisdictions, particularly the ACT and Victoria.
"We are also deeply concerned about the government's intention to launch a new website that will outline how much federal funding each school in Australia will receive under the proposed changes.
"The website ignores the reality of Catholic education systems and indeed other schools systems, which are able to redistribute funds according to the needs of individual students or schools.
"The website will effectively undermine system autonomy and mislead parents who could easily assume that the Commonwealth will now determine Catholic school fees."
Some families with children in Catholic schools are receiving letters from school authorities saying fees will have to go up.
Shadow Treasurer Chris Bowen said he had been briefed by the Catholic Education Office that school fees in his Sydney electorate of McMahon would have to double. He said the schools package was "being rammed down the throat of this very important part of the education sector".
Meanwhile, Morrison has announced the government will make good by June 30 on its promise to crossbencher Nick Xenophon to give pensioners a supplement to help with energy costs.
The government promised AUD75 for a single person and AUD125 for a couple as part of the deal to get the support of the Nick Xenophon Team for the company tax cut for businesses with an annual turnover of up to AUD50 million, with the timing of the payment to be announced in the budget.
Morrison said the payment would go to aged pensioners, disability support pensioners, those on parenting payments, and veterans.
By Michelle Grattan, Professorial Fellow, University of Canberra
Liked this? Then you'll love these…
Australia: Student journos cry foul after being barred from Budget lock-up
Australia: Fee hikes in universities as govt prepares to slash funds


text:
Das Internationale Hydrologische Programm (häufig auch International Hydrological Programme; abgekürzt IHP) ist ein wissenschaftliches Aktionsprogramm der UNESCO.

Struktur des Programms

Aus der Internationalen Hydrologischen Dekade der UNESCO, die von 1965 bis 1974 terminiert war, entstand das IHP als langfristiges wissenschaftliches Programm. Anfangs stark technisch ausgerichtet, hat das Internationale Hydrologische Programm mit der erkannten Bedeutung einer auch sozialwissenschaftlichen Erforschung in den letzten Jahren eine stark fachübergreifende Ausrichtung erhalten. Auch die UNO möchte (z. B. Internationale Aktionsdekade "Wasser – Quelle des Lebens") das Bewusstsein für die nachhaltige Nutzung, den Schutz und die Verwaltung der Weltwasserreserven in ihrer Bedeutung fördern. Zu diesem Prozess erbringt das Internationale Hydrologische Programm wichtige wissenschaftliche Beträge und Impulse. Daneben ist das Programm führend bei der Erarbeitung von wissenschaftlichen Grundlagen zur Bewertung der weltweiten Wasserreserven und an der Erstellung des ersten Weltwasserberichts beteiligt.

Das Programm umfasst drei Schwerpunkte:
 die Erforschung des Wasserkreislaufes in einer sich ständig verändernden Umwelt,
 die Bewirtschaftung der Wasserressourcen mit dem Ziel einer nachhaltigen Entwicklung
 die Förderung eines entsprechenden Capacity Building, vor allem in den Ländern des Südens (Aus- und Fortbildung).

Für das Programm stehen der UNESCO selbst jährlich etwa 8,5 Millionen US-Dollar (Budget für 2004/2005) zur Verfügung. Das Programm wird weitgehend mit zusätzlichen inhaltlichen Beiträgen der Mitgliedsländer umgesetzt.

Die Fortschreibungen am Internationalen Hydrologischen Programm sind durch Phasenprogramme mit definierten Themenschwerpunkten bestimmt. Thema der Programmperiode 2002–2007 war: "Water Interactions: Systems at Risk and Social Challenges".

Aktuelle Schwerpunkte

Die mit dem Konzept der Wassersicherheit behandelbaren Fragestellungen reichen von der Entwicklung von Strategien über das Management von Wasserressourcen in Katastrophenfällen bis hin zum Umgang mit Wasserkonflikten unter konkurrierenden Nutzern und Nutzungen und zielen auf eine nachhaltige gerechte Verwaltung der Wasserressourcen unter Einbeziehung physischer und sozialer Faktoren ab. Dieses Ziel soll durch den Ausbau der Forschungsinfrastruktur, verstärkte Forschungstätigkeit und die effiziente Verbreitung der Ergebnisse unter politischen Entscheidungsträger und Planungsstellen erreicht werden.

Inhaltlicher Schwerpunkt ist die Etablierung der Wasserökologie. Mit ihr als methodologischer Grundlage soll sowohl die Gefährdung von aquatischen Ökosystemen verringert als auch die Effizienz und Nachhaltigkeit der Wasserwirtschaft verbessert werden. Insbesondere in Entwicklungsländern wird Strategien zur effizienten Bewirtschaftung von Wasser und Ökosystemen im städtischen Bereich eine entscheidende Bedeutung zukommen. Neuartige Technologien und innovative institutionelle Ansätze zur Wasserwirtschaft, die die Grundlage für den Schutz und die effizientere Nutzung der Wasserressourcen darstellen, sollen vorerst in Biosphärenreservaten erprobt werden.

Nationale Beiträge zum Programm

Der deutsche Beitrag wird durch ein Nationalkomitee betreut, dessen Geschäftsführung das IHP/HWRP-Sekretariat bei der Bundesanstalt für Gewässerkunde übernommen hat. Das deutsche IHP-Nationalkomitee wirkt durch die Austragung internationaler Veranstaltungen, die Erstellung von State-of-the-art Reports, durch Veröffentlichungen und die Mitarbeit in internationalen Arbeitsgruppen an der Programmdurchführung mit. Der österreichische Beitrag ist das Programm Hydrologie der Österreichischen Akademie der Wissenschaften.

Neben dem IHP existieren weitere langfristige UNESCO-Programme zur Erforschung und zum Schutz der Lebensumwelt des Menschen. Dazu gehört die Zwischenstaatliche Ozeanografische Kommission (IOC), das Programm "Der Mensch und die Biosphäre" (MAB) und das Internationale Geowissenschaftliche Programm (IGCP).

Siehe auch
 UN-Wasser
 Weltwasserforum, Weltwasserrat, Weltwassertag

Weblinks
 Bedrohte Zukunft: Schwarzbuch Wasser
 Water-Portal der UNESCO (englisch)
 IHP/HWRP-Nationalkomitee (PDF; 142 KB) – Internationales Wasserzentrum
 UNESCO-Wasserprogramme – Deutsche UNESCO-Kommission

Aktion oder Projekt der Hydrologie
UNESCO

redpajama_set_name: RedPajamaWikipedia


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Rather than leaving the plumbing repair marks on the floor permanently, you can elevate your building and repair the piping and draining system.
Decrepit houses need no longer be demolished, but lifted to longevity.
Copyright © 2013 - All Rights Reserved - TDBD ENGINEERING WORKS Pvt. Ltd.


text:
Catawiki Cameras & Computers Computers & Video Games Computer Collectables Auction
Asus white Asus Eee PC 1005HA-M - Desktop
The white Asus Eee PC 1005HA-M has a 101.1 inch screen (matte, 1024 x 600 pixels), an Intel Atom N270 processor (1.6 GHz), 1 GB of RAM, 160 GB of hard drive and Windows 7 starter operating system.
The selection of ports in the current model is the same as in previous versions.
On the left of the netbook we find the power supply input, a VGA port, a USB 2.0 port and a Kensington lock slot that can be used to protect the netbook from thieves. The VGA output allows you to connect to a compatible screen or flat screen TV.
The remaining doors are on the opposite side, on the right. We find two more USB 2.0 ports for peripherals, an RJ-45 network port (10/100 Mbit Ethernet), audio ports (headphones and microphone) as well as the 2-in-1 card reader that supports memory formats SD (SDHC) and MMC.
The front and back of the netbook are free of ports. The tapered design leaves no room for ports on the front, while the back is occupied by the 6-cell battery. On the front there is just the space for the two stereo speakers.
The layout of the doors is practical. The USBs are not too close together, allowing you to insert two memory sticks side by side easily. The choice of ports has not been extended in the model with Windows 7. As before, the integrated card reader only supports SD (SDHC) and MMC. We would have preferred a card reader with support for multiple formats.
Like the Atheros AR8132 Ethernet module, the Eee PC is equipped with a WLAN n standard 802.11 a / g / n (Atheros AR9285). Bluetooth is not included in this cheap 1005HA-M, but for around 50 Euros more you can buy the 1005HA-H which is equipped with a Bluetooth 2.1 + EDR module.
Regarding the software, Asus only offers the basic set-up. The Windows 7 Starter (32-Bit) operating system is now pre-installed; it offers only the strictly vital functions of Windows 7, thus offering a great improvement in loading times on netbooks. In addition, Asus has pre-installed various programs for daily use; you will find Adobe Reader 9, E-Cam camera software, Skype, a 60 day trial version of Microsoft Office 2007 (Excel 2007, OneNote 2007, PowerPoint 2007 and Word 2007), Trend Micro Internet Security, some games and various utilities from Asus developed specifically for the Eee line. These include the 'Eee Docking' toolbar which extends the desktop when needed, giving quick access to various Asus programs.
Asus used a 10.1-inch matte LED-backlit screen in the Eee PC 1005HA-M, the same as in previous versions. The matte screen is a great advantage, as only the display frame is reflective in this way, making outdoor work easier. But with low brightness it becomes difficult to distinguish the images on the screen. The native resolution is 1024 x 600 pixels. A higher, interpolated resolution of 1024 x 768 can no longer be selected with Windows 7. An external monitor or flat screen TV can be connected to the netbook through the integrated VGA port.
Like its predecessors, this netbook is equipped with an Intel Atom N270 processor clocked at 1.6 GHz, 512 KB of L2 cache and 533 MHz FSB (Front Side Bus). It also has 1 GB DDR2 integrated RAM. Memory is sufficient even with Windows 7 Starter, but an upgrade to 2GB can be a good investment, as some applications already pull the integrated 1GB module to its limits used on Windows 7. All in all the Eee PC 1005HA-M with Windows 7 it is convincing, with its speed of execution in everyday use. Startup is quick and Windows reacts quickly to commands. HD movies cannot be played smoothly with only 1GB of RAM; and when multiple programs are used at the same time, the netbook quickly reaches its processing capacity limits, and loading times lengthen. But the 1005HA-M is properly equipped to handle everyday computer use, excluding photo and video manipulation.
For more performance or longer battery life, Asus has included a program called 'super hybrid engine'. This allows you to adjust the clock speed of the Intel Atom N270. In 'power saving' mode the CPU can be raised to 1280 MHz while for high performance the processor can be overclocked to 1680 MHz. This elevates the N270 to the levels of the N280.
Asus Eee PC 1005HA-M - long battery life. According to Asus, the maximum autonomy is 8 and a half hours, even with the new Windows 7. With minimum screen brightness and all energy saving settings activated it stops after 9 hours and 13 minutes (Battery Eater readers test), a improvement of 43 minutes. With maximum screen brightness, a balanced performance profile and using the LWAN, the 1005HA-M achieves a good realistic time of 7 hours and 5 minutes. Since the netbook does not have a DVD player, we have left out the video test. Under heavy load and with maximum brightness (Battery Eater's classic test) it held up for a respectable 5 hours and 28 minutes.
Series/ Model
white Asus Eee PC 1005HA-M
26×178×262 mm


text:
Once again, it seems like the Liberals are trying to control the message
By Rob Breakenridge — Global News — Aug 3 2019
It was a story this week that felt awfully familiar: the Liberals using their majority on a Commons committee to block an investigation into a potentially embarrassing or even scandalous matter.
When it was first reported that opposition members on the foreign affairs committee were bringing forward a motion calling on the committee to look into whether the government attempted to muzzle two former diplomats with regard to the China situation, I think we all knew how things were going to proceed.
The views, opinions and analyses expressed in the articles on National Newswatch are those of the contributor(s) and do not necessarily reflect the views or opinions of the publishers.


CFC casually meets at 9AM every Sunday morning at Lowdermilk Park (By the North Gazebo near the sand volleyball courts). Enjoy views of the ocean, sand beneath your feet, friends and fellowship along with an inspirational time of reflection.
CFC also meets every Thursday at the FLEISCHMANN PARK COMMUNITY center 6:30 - 7:30PM for Bible Study.


Lindsey office furniture is going to undoubtedly be more great as it perfectly shinning. There are lindsey office furniture several easy and practical tricks to produce the dark wood cabinets looks perfect. Easy and simple way to get the dim timber seems lindsey office furniture shinning is to get it polished. Instead of painting the cabinets, you'll lindsey office furniture be able to let the organic dark coloring of their timber revealed. Use transparent polish. It can protect the cabinet as well as make it longer shinny.
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Second, check the sockets far too; if you will locate some breaks or cracks lindsey office furniture birmingham al of the lighting fixture. Over-heat is also the usual reason for these cracks. Alter the fixture also when you detect some fractures on the sockets. To confirm the fixture's power, you need to work with a circuit expert. In the event you think that replacing all fixtures will likely soon be pricey, then change the ballast of this Lindsey office furniture only. It is rather simple job, but you have to understand about a few comprehension associated with electric program.
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text:
Q: Showing Depth in Three.js Point Cloud I have a 3D scan of a rock that I have represented as a point cloud using Three.js, and I wanted to more explicitly show the features of the rock, perhaps using lighting to show depths with Three. Viewing the rock from the top, I see this:

When looking closely from the side, you can see rigid features that I would like to show more closely:

I am unsure in how to approach this, and was hoping for some help in showing these rock features within this visualization. For context, this is my current shader setup:
vertexShader: `
    precision mediump float;
    varying vec3 vColor;
    attribute float alpha;
    varying float vAlpha;

    void main() {
        vAlpha = alpha;
        vColor = color;
        vec4 mvPosition = modelViewMatrix * vec4( position, 1.0 );

        gl_PointSize = 2.0;

        gl_Position = projectionMatrix * mvPosition;
    }
`

fragmentShader: `
    #ifdef GL_OES_standard_derivatives
    #extension GL_OES_standard_derivatives : enable
    #endif
    precision mediump float;
    varying vec3 vColor;
    varying float vAlpha;

    void main() {
        float r = 0.0, delta = 0.0, alpha = 1.0;
        vec2 cxy = 2.0 * gl_PointCoord.xy - 1.0;
        r = dot(cxy, cxy);

        //#ifdef GL_OES_standard_derivatives
        delta = fwidth(r);
        alpha = vAlpha - smoothstep(vAlpha - delta, vAlpha + delta, r);
        //#endif
        if (r > 1.0) {
            discard;
        }
        gl_FragColor = vec4(vColor, alpha);
    }
    //varying vec3 vColor;
    //varying float vAlpha;
    //void main() {
        //gl_FragColor = vec4( vColor, vAlpha );
    //}
`

I am creating my point cloud using THREE.Points, with a BufferGeometry geometry and ShaderMaterial material.
Is there a way to go about this more explicitly showing my point cloud depths?
Thank you!

A: The depth of the current fragment is stored in the .z component of gl_FragCoord. The depth is stored in range [0.0, 1.0] (except this range is changed by glDepthRangef):
With that information you can set the alpha channel of the point, with decreasing opacity, by its depth:
float depth = gl_FragCoord.z;
gl_FragColor = vec4(vColor, 1.0 - depth);

Since the depth at perspective projection is not linear (see How to render depth linearly in modern OpenGL with gl_FragCoord.z in fragment shader?), it would be nice to have a value in rage [0.0, 1.0], which linearly represents the depth of the point between the near and the far plane.
THis can be done by the function LinearizeDepth in the following example:
uniform vec2 u_depthRange;

float LinearizeDepth(float depth, float near, float far)
{
  float z = depth * 2.0 - 1.0; // Back to NDC
  return (2.0 * near * far / (far + near - z * (far - near)) - near) / (far-near);
}

void main()
{
    // [...]

   float lineardepth = LinearizeDepth(gl_FragCoord.z, u_depthRange[0], u_depthRange[1]);
   gl_FragColor = vec4(vColor, 1.0 - lineardepth);
}

To make the example tun, the depth range has to be set to the uniform u_depthRange. The near plane of the PerspectiveCamera is stored in the .x component and the far plane in .y component:
var near = 1, far = 10

camera = new THREE.PerspectiveCamera(fov, width / height, near, far);

var uniforms = {
      // [...]
      u_depthRange: {type: 'v2', value: {x:near, y: far}}
};

var material = new THREE.ShaderMaterial({
      uniforms: uniforms,
      // [...]
});

Note, for a "good" effect, the near and the far plane of the camera, have to be as close to the geometry as possible!

redpajama_set_name: RedPajamaStackExchange


New Photoshoot by Roberta Scroft[Crazy, Stupid, Love],2011. . Wallpaper and background images in the Ryan Gosling club tagged: ryan gosling photoshoot 2011 crazy stupid love.
This Ryan Gosling photo contains park bench, hip boot, and thigh boot. There might also be sign, poster, text, chalkboard, business suit, street, city scene, and urban setting.


text:
March 8, 2019 - Updated on April 17, 2019
Russia: Duma makes "disrespecting" the state punishable by imprisonment
Condemning abuses
Judicial harassment
Credit: Yuri Kodobnov / AFP
Читать на русском / Read in Russian
Reporters Without Borders (RSF) calls on the Russian parliament to abandon its headlong pursuit of increasingly draconian legislation after the Duma (lower house) passed a package of laws yesterday with harsh penalties for "fake news" and "disrespecting" the state.
In a day in which it lived up to its nickname of the "mad printer," the Duma adopted laws that make "disrespect towards the state or society" punishable by up to 15 days in prison and "dissemination of fake news" punishable by astronomical fines, and allow the authorities to block such content immediately without reference to a judge.
The new laws will serve as additional weapons for use by Russia's prosecutors and judges – whose lack of independence is notorious – in silencing criticism. The laws still need to be approved by the upper house and signed by President Putin, but this is just a formality, and they will take effect as soon as they are published in the official gazette.
"By constantly trampling on fundamental freedoms, Russia's legislators are themselves at the forefront of disrespect for society and the state," said Johann Bihr, the head of RSF's Eastern Europe and Central Asia desk. "Protecting the constitution would require dismantling the arsenal of repressive legislation, not reinforcing it. If nothing stops this headlong advance, the Duma will soon have revived the Soviet penal code."
Two of the four new laws target content that "expresses a manifest disrespect for society, the state, its official symbols, its constitution or its organs of power (...) in an unseemly manner that offends human dignity and public decency."
This new offence will be punishable by fines of up to 100,000 rubles (1,350 euros) for first offenders, and 300,000 roubles (4,000 euros) or 15 days in prison for repeat offenders. And the offending content will have to be taken down or blocked within 24 hours if so required by the prosecutor's office.
The two other new laws penalize "the dissemination of patently inexact information of general interest" that could be "prejudicial to citizens, their property, safety or public order or disrupt the functioning" of infrastructure. The penalties range from fines of up to 400,000 rubles (5,400 euros) for individuals to fines of 1. 5 million rubles (20,000 euros) for entities. Offending content will be subject to "immediate" removal or blocking.
The broad wording of the new laws and the automatic nature of the content blocking give the authorities a great deal of leeway to use them selectively and politically. Far from responding to a legal void, these provisions will just contribute to an already well-stocked repressive legislative arsenal.
The laws on insult and defamation have also become steadily harsher in recent years and "insulting a state representative" is heavily penalized. Although recently eased, the anti-extremism legislation is widely used to silence critics of how Russian society and state are currently organized, and control of the Internet has become much tighter since 2012.
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Michael Shainblum - Talking Time-Lapse With The Master Of Visuals
Our guest in this episode is world-renowned film maker and photographer Michael Shainblum. I invited Michael on to the podcast to ask him all about his stunning NYC time-lapse video, and how he captured the essence of the city so effectively in it.
Over the course of our conversation Michael shared some great insights into his technical and creative process and offered tips and advice for anyone wanting to try time-lapse photography for themselves.
MastersOfVisuals.com – The ultimate time-lapse photography masterclass, by Michael Shainblum
ShainblumPhoto.com – Visit Michaels website for articles, videos, tutorials, workshops and more
Michael's main goal when creating time-laspe videos
How Michael captured the feeling of the city in his NYC time-lapse
How the entire video was built around one scene and Michael created a progression to build towards it
What it was like trying to film all around NYC and 1 World Trade
The #1 most important thing every new piece must have before Michael puts it out into the world
What kind of motion works best for time-lapse
What kind of scenes work best
How time-lapse allows you to see things you weren't noticing in person
What equipment do you need to get started with time-lapse
What should you focus on when creating time-lapse videos for the first time
The most important thing to get right in your time-lapse videos
Tips for getting started with sunset/sunrise time-lapse transitions without needing extra gear


Born around 460 BC. BC on the island of Kos and died around 370 BC. AD in Larissa, is a Greek physician of the Periclean century, but also a philosopher, traditionally considered the "father of medicine" because he is the oldest Greek physician in whom historians have sources, these are largely legendary and apocryphal .
He founded the Hippocratic Medicine School that revolutionized medicine intellectually in ancient Greece, establishing it as a discipline distinct from the other disciplines of knowledge to which it had traditionally been united (including theurgy and philosophy), thus making medicine a profession In herself.


Once you have read this post please forward it to your state governor's office.
Improving school performance is one of the best investments a governor can make to improve the economic well-being of their state. If all fifty states had improved their student outcomes to match eighth place Minnesota students, the US economy would be four times what it is right now.
Since the ESSA legislation of December 2015 education policy is now the responsibility of the state rather than the federal government. So, it will be those state that do the best job of preparing its students for the new economy that will attract the best paying jobs.
This has become even more important since young people are staying home far more than they used to. Almost 80% of all American high school graduates stay in the community in which they were raised. They will need jobs.
When an employer is deciding to expand or start a business in a given location, one of the major factors in that decision is going to be the suitability of the high school and college graduates. According to Professor Eric Hanushek of Stanford University and the Hoover Institute, "Mississippi 12th grade students have the equivalent skills of a 9th grader in Massachusetts." So, even if Mississippi offers generous tax incentives to attract that company, the company's shareholders will probably prefer not having to pay to retrain people just to employ them. If the employer only wishes to employ workers to perform menial tasks then the situation is different. That employer will try to automate as many tasks as possible first, and failing that will hire low-skill, low-wage laborers. The state will have trouble growing the economy that way.
Schools play a vital part in the economic well-being of all communities.
The economic viability of your country, county or city is directly linked to how well your schools perform their mission.
Needless to say the National Governors Association sprang into action and issued their own response in 1989 and pledged that U.S. students would lead the world in math and science achievement by 2000. When the results came out for 2000 the USA was ranked 18th In Math and 14th in Science. By 2015 the USA had dropped TWENTY TWO places in Math and NINE places in Science to 40th in Math and 25th in science. It is worth noting that Math and Science abilities are the best predictor of future earnings.
It is also worth noting that fear is a terrible motivator, and our teaching-to-the-tests relies on fear. Plato tells us that "Knowledge which is acquired under compulsion has no hold on the mind." Studies have shown that stress in students is worse than their parents' experience.
The USA has prospered despite this situation due to pockets of excellence in education where partnerships exist between the local education infrastructure, venture capital, employers and immigrants like me. Most notably in Silicon Valley. That should give us a clue how to move forward.
Conclusion? Boosting cognitive skills is the only way to have an effect on a nation or state's economic well-being. The bad news is that the cognitive skills of American students has barely moved since the sixties.
Another way of saying cognitive skills is thinking skills.
The question before us is not whether we should improve our schools but how: especially in teaching cognitive abilities. .
Currently our schools exclusively teach-to-the-tests. That means we teach our children how to memorize and regurgitate facts. If they can do that sufficiently well we award them a diploma. Surely the purpose of schools is to do more than that. Especially since memorization is NOT what employers need. Increasingly they are investing in machines that recall the facts needed to perform repetitive tasks. They need people trained to do the tasks that machines do not do – creative and critical thinking, collaboration in search of solutions to problems and communications.
Now more than ever in this age of automation and robotization we should be adhere to the true purpose of education: teaching students to "Make Meaning." In order for anyone to make meaning out of the mountains of data that come their way they need to be able to think. And thinking has yet to appear on America's school curriculum. In fact, as one school superintendent told me to the applause of others in the room after I had delivered an address to them; "It's illegal for us to teach critical thinking!" He was using hyperbole to make a point. But he has a point. The way it works today if a school goes off-curriculum they will be punished; even if they are teaching the skills necessary for good jobs and the economic well-being of the community they serve.
This leads to one inevitable conclusion: the goal of school reform must be a radical re-think to avoid long-term decline. If we dropped from 18th to 40th in math in fifteen years, what else but further declines can we expect if we keep doing the same things over and over and expecting a different result? A simple graph continuing this downward trajectory shows that the USA will be 70th in the world in Math by 2030. The current occupant of that position is Tunisia. And Math is the best predictor of future earnings!!
If reformers simply tinker, schools will not go away; they will become less relevant to all their stakeholders; from the local community on up to the international level. We cannot let this happen; it's a national security issue right up there with terrorism and climate change.
In summary: If you want a prosperous community do a better job of teaching thinking or cognitive skills.
One of humanity's greatest discoveries has lain hidden for 2400 years. Socrates knew that it is our ability to think that has ensured our evolution. Descartes knew 350 years ago that it's what makes us who we are. It's time to honor them and their discoveries by emulating them.
Begin by understanding that the twin engines of thinking are: Questioning like Socrates and Doubting – or Healthy Skepticism – like Descartes. Get students in a group and have them follow this guideline: 1) Ask a question. 2) Answer the question. 3) Question the answer. 4) Question the next few answers. 5) Agree on an opinion. 5) Communicate that opinion.
Employers of all kinds demand candidates with four skills: Critical Thinking, Problem Solving, Communications, and most of all Collaboration or Team Working skills. As everyone from the last president on down has noted, schools are still supplying students for old-world skills: stressing memorization, repetition and test taking. If, as President Obama's Education Secretary Arne Duncan once observed, "We must educate our way to a better economy," then America urgently needs to address this imbalance.
How? Read this Teachers' Manifesto and share it with some other teachers. Start your own "TEACH UP" – three teachers who care talking at Starbucks – and use Social Media for the good of all students by sharing what it's like to give students some power over their own education. It will catch fire.


Grab your WAEbands and yoga mats and get ready for some AMAZING workouts with Kate! 45 minutes of PiYo LIVE, with a cool-down and 15 minutes of Reiki Healing with Bernadette! Classes will be held in Norton, MA!! Register HERE peeps!