text:
\section{Overview}
\label{sec:overview}

\figref{overview} illustrates an overview of our architecture.
Our system takes a masked panoramic image ${\mathbf{I}}_{in}$ and the corresponding binary mask $\mathbf{M}$ as inputs and generates the inpainted panoramic image ${\mathbf{I}}_{out}$.
The masked panoramic image is generated by ${{\mathbf{I}}_{in} = {\mathbf{I}}_{gt} \odot(\mathbf{1}-\mathbf{M})}$, where ${\mathbf{I}}_{gt}$ represents the ground-truth panoramic image and $\odot$ denotes the Hadamard product.
Our system first utilizes an off-the-shelf model to estimate the room layout ${\mathbf{L}_{m}}$ from input masked panoramic image.
This layout map is then concatenated with ${\mathbf{I}}_{in}$ and $\mathbf{M}$ to obtain a five-channel input map fed into the generator $\mathbf{G}$.
We further derive two semantic segmentation maps ${\mathbf{L}_{3-class}}$ and ${\mathbf{L}_{p-wise}}$ using the layout map for the subsequent normalization module (\secref{layout_guidance}).
The image generation model follows the conventional generative adversarial architecture with one content encoder and one image decoder with one discriminator. (\secref{inpainting_backbone}).
To impose structure information during inpainting, we introduce a plane-aware normalization that modifies the SEAN~\cite{Zhu_2020_CVPR} block with two semantic segmentation maps to guide the decoder with spatially varying normalization parameters per structural plane (\ie ceiling, floor, and individual walls).
Such a plane-aware normalization provides useful guidance for global structure preservation as well as consistent local image content generation (\secref{planar-aware_module}).
Finally, common loss functions in image inpainting, including the reconstruction loss, the perceptual loss, the style loss, and the adversarial loss are employed to train our model (\secref{loss_func}).
\section{Experiments}
\label{sec:results}
In this section,  we evaluate the performance of our model by comparing it with several state-of-the-art image inpainting approaches and conducting ablation studies to verify the necessity of individual components in the proposed architecture.
Please refer to our online webpage for other experiments and more results\footnote{\url{https://ericsujw.github.io/LGPN-net/}}.

\subsection{Experimental Settings}
\heading{Dataset and baselines.}
We compare our model with the following state-of-the-art structure-aware image inpainting models:
\begin{itemize}
    \item EC~\cite{nazeri2019edgeconnect}: a two-stage adversarial network that comprises an edge completion model followed by a generator.

    \item LISK~\cite{jie2020inpainting}: a multi-task learning framework that exploits image structure embedding and an attention mechanism in the generator.

    \item PanoDR~\cite{gkitsas2021panodr}: a deep learning framework that combines image-to-image translation with generator to condition the inpainting on the indoor scene structure.
\end{itemize}
The experiments were conducted on a public indoor panorama dataset, Structured3D~\cite{Structured3D}, which contains 21,835 indoor panoramas.
The official data split is adopted for training(18,362), validation(1,776), and testing(1,697).
We follow the same procedure as PanoDR to generate mask images using contours of foreground furniture (see Section 3.1).
We use the officially released implementation of baselines for training from scratch and testing.
Note that each indoor panorama in Structured3D has two representations of the same scene (\ie empty and furnished).
Therefore, the experiments were conducted in two phases to evaluate our model and baselines in different application scenarios (\ie structural inpainting vs. furniture removal).

\heading{Evaluation metrics.}
We take several commonly used image quality assessment metrics in previous inpainting approaches for quantitative evaluation.
Specifically, we used the low-level feature metrics, including Mean Absolute Error (MAE), Peak Signal-to-Noise (PSNR), Structural Similarity Index (SSIM)~\cite{1284395},
and Fréchet Inception Distance (FID)~\cite{heusel2018gans}.

\heading{Implementation details.}
We implement our model in PyTorch and conduct the experiments on a single NVIDIA V100 with 32G VRAM.
The resolution of the panoramic images is resized to $512 \times 256$.
We use Adam~\cite{kingma2017adam} optimizer in the training process with the hyper-parameters setting of $b_{1} = 0.0$ and $b_{2} = 0.9$, a learning rate of $0.0001$, and a batch size of 8.
We empirically set $\lambda_{rec} = 1$, $\lambda_{perc} = 0.1$, $\lambda_{sty} = 250$, $\lambda_{G} = 0.1$, and $\lambda_{D} = 0.5$ in the total loss function (\eqnref{total_loss}).
For HorizonNet~\cite{SunHSC19}, we use the official pre-trained model for layout estimation.

\subsection{Evaluation on the Empty Scenes}
In this experiment, we evaluate both the qualitative and quantitative performance of our model on the image inpainting task by comparing it with baselines.
The qualitative comparisons are shown in~\figref{comp_sota_si_fr} (top 8 rows).
In contrast to EC and LISK, which fail to restore image structures in the masked regions, our method faithfully generates image contents adhering to the underlying layout structure.
While PanoDR shows slightly better structure preservation than EC and LISK, it fails to generate image contents consistent with the surrounding of masked regions as our method does.
Therefore, our method achieves the best performance against all the baselines across all evaluation metrics as shown in~\tabref{result_comp} (top).

\subsection{Evaluation on the Furnished Scenes}
Furniture of irregular shape will more or less obscure the layout of the indoor scene, making it more challenging to restore the regular structure in the missing area.
Therefore, in this experiment, we would like to evaluate how well our model learned from empty scenes can generalize to the furnished scenes.
Since the inpainting task setup here exactly matches the one defined in the PanoDR, we use the pre-trained model of PanoDR in this experiment for a fair comparison.
As shown in~\figref{comp_sota_si_fr} (bottom 8 rows), our method still clearly outperforms baselines in generating image contents that align the layout structure well and are consistent with the surrounding of the masked regions.
The quantitative results are shown in~\tabref{result_comp} (bottom).
It is worth noting that the way PanoDR performs image completion via compositing the predicted empty image and input image using the object mask will lead to severe artifacts where occlusion occurred between foreground objects (see~\figref{FR_SI}(PanoDR)).
\input{Figures/tbl_empty_full}

\subsection{Ablation Study}
Here, we conduct ablation studies to validate our model from different perspectives.
First, we evaluate the necessity of individual design choices in our architecture.
Then, we conduct two experiments to evaluate how sensitive our model is to the size of input masks and the quality of input layout maps.
\input{Figures/fig_ablation_new}
\input{Figures/tbl_ablation}

\heading{Ablation on network architecture.}
In this experiment, we start with the backbone model ($\mathbf{Backbone}$) as the baseline, then progressively adding only layout guidance map (${\mathbf{Layout\ map\ only}}$), and our plane-aware normalization ($\mathbf{Full\ model}$).
As shown in~\tabref{ablation_study}, we obtain the best performance with the full model on all the metrics.
The qualitative comparisons shown in~\figref{ablation_new} indicate that adding layout guidance map generates clear structure boundaries in the final result ($2^{nd}$ and $3^{rd}$ columns), while our full model with plane-aware normalization can constrain the image generation to the adjacent structural planes and obtain visually consistent results ($3^{rd}$ and $4^{th}$ columns).

\heading{Sensitivity to the mask size.}
In this experiment, we analyze the testing dataset and classify the images into different categories according to the area proportions of input masks. \tabref{add_exp1} shows the inpainting performance for each category. We can tell that the inpainting quality degrades with the increasing mask size. A significant drop occurs where the ratio of input mask is greater than 30{\%}.

\heading{Sensitivity to the layout estimation.}
In order to explore the effect of the accuracy of layout estimation on the inpainting quality, we first devise a mechanism to generate layout maps with different levels of accuracy.
Specifically, we feed masked images of different mask sizes into HorizonNet. We start by generating randomly located rectangle masks of 5\% image size and increase the mask ratio to 10\%, 30\% and 50\% to deliberately produce layout structures with decreasing quality.
Then we take these layout maps as conditional inputs of our model and compare the inpainting performance empty-room testing dataset.
As shown in~\tabref{add_exp2}, our model degrades marginally when the quality of estimated layouts decreases from 0.96 to 0.84, indicating our model is robust to the varying input layout maps.
\input{Figures/tbl_addtion_mask}
\input{Figures/tbl_addtion_layout}
\input{Figures/fig_real_scene}

\subsection{Qualitative Results on Real-world Scene}
Real-world scenes have complex lighting and layout structure.
However, the amount of data in the real-world scene dataset and the quality of furniture category annotations are insufficient for training our model, so we choose to train on the synthetic dataset Structured3D~\cite{Structured3D}.
Nevertheless, we still compare our results with PanoDR~\cite{gkitsas2021panodr}, which also implements the furniture removal task, on the real-world scene dataset.
Since the real-world scene dataset does not contain paired data (\ie scenes before and after furniture removal), quantitative evaluation is infeasible and we can only provide qualitative comparisons here.
\figref{real_scene} shows that our inpainted results have a higher quality of structural maintenance and color restoration. Moreover, compared with PanoDR, we can still exert more stable performance in real-world scenes.
Please refer to our online webpage for more results\footnote{\url{https://ericsujw.github.io/LGPN-net/}}.

\section{Introduction}
\label{sec:intro}
Image inpainting is a widely investigated topic in computer graphics and vision communities, which aims at filling in missing regions of an image with photorealistic and fine detailed content.
It plays a crucial step toward many practical applications, such as image restoration, object removal, \etc.
With the rapid development of deep learning, image inpainting has been revisited and improved significantly in the past few years.
A considerable body of researches has been explored to generate impressive results on perspective datasets.

In this work, we address the image inpainting problem in the context of indoor panoramas.
Indoor panoramas provide excellent media for the holistic scene understanding~\cite{LNCS86940668panocontext} that would further benefit several applications such as object detection, depth estimation, furniture rearrangement, \etc.
In particular, removing foreground objects and filling the missing regions in an indoor panorama is essential for the interior redesign task.
However, the complex structures and textures presented in the indoor scenes make the inpainting problem non-trivial and challenging for previous methods.
As shown in~\figref{FR_SI}(EC), results generated by a state-of-the-art deep learning method fail to align the image structure along the layout boundaries and produce inconsistent blurry image contents.

Recently, Gkitsas~\etal~\cite{gkitsas2021panodr} introduced PanoDR, a diminished reality-oriented inpainting model for indoor panorama.
The main idea is to translate a furnished indoor panorama into its empty counterpart via a network that leverages both a generator and an image-to-image translation module.
The inpainting result is then obtained by compositing the predicted empty panorama and input panorama using the object mask.
However, there are still obvious artifacts near the boundaries of masked regions as shown in~\figref{FR_SI}.
\input{Figures/Figure_1_ver2}

To achieve better inpainting quality, we present an end-to-end deep generative adversarial framework that exploits both the global and local context of indoor panoramas to guide the inpainting process.
Specifically, we take the low-level layout boundaries estimated from input panorama as a conditional input to guide the inpainting model, encouraging the preservation of sharp boundaries in the filled image.
A plane-aware normalization module is then employed to embed local plane-wise style features derived from the layout into the image decoder, encouraging local texture restoration from adjacent room structures (\ie ceiling, floor, and individual walls).
We train and evaluate our model on a public indoor panorama dataset, Structured3D~\cite{Structured3D}.
Experimental results show that our method produces results superior to several state-of-the-art methods (see~\figref{teaser}, \figref{FR_SI} and \figref{comp_sota_si_fr}). The main contributions are summarized as follows:
\begin{itemize}
\item We present an end-to-end generative adversarial network that incorporates both the global and local context of indoor panoramas to guide the inpainting process.
\item We introduce a plane-aware normalization module that guides the image decoder with spatially varying normalization parameters per structural plane (\ie ceiling, floor, and individual walls).
\item Our method achieves state-of-the-art performance and visual quality on synthetic and real-world datasets.
\end{itemize}

\section{Related Work}
\label{sec:related}

\heading{Traditional image inpainting.}
There are two main genres among traditional image inpainting works: diffusion-based methods and patch-based methods.
Diffusion-based methods~\cite{mumford,10.1145/364338.364405,935036,Drori2003FragmentbasedIC,Liang2015AnEF,wei2016} propagate pixels from neighboring regions to the missing ones to synthesize the image content.
On the other hand, patch-based methods~\cite{patchmatch,6960838,7180400,7922581,10.1007/s11042-017-4509-0,8352516,10.1007/s11042-017-5077-z} fill the missing regions by searching for and copying similar image patches from the rest of the image or existing image datasets.
Without a high-level understanding of the image contents, these methods easily fail on images with complex structures.

\heading{Learning-based image inpainting.}
With the rapid development of deep learning, several image inpainting techniques based on convolutional neural networks (CNN) have been proposed.
These methods aim to learn a generator from a large dataset to produce photorealistic image contents in the missing regions effectively.
Context Encoders~\cite{pathakCVPR16context} pioneers CNN-based image inpainting by proposing an adversarial network with an encoder-decoder architecture.
However, due to the information bottleneck layer of the autoencoder, the results are often blurry, incoherent, and can not work on irregular masks.
Yu~\etal~\cite{yu2018generative} proposed a coarse-to-fine network and a context-aware mechanism to reduce blurriness.
Iizuka~\etal~\cite{IizukaSIGGRAPH2017} adopted local and global discriminators and used dilated convolutions to increase the model's receptive field and enhance coherence.
Liu~\etal~\cite{liu2018partialinpainting} proposed partial convolutions, which only consider valid pixels during convolution, to handle irregular masks.
Yu~\etal~\cite{yu2018free} further extends the partial convolutions by introducing a dynamic feature gating mechanism, named gated convolutions, to deal with free-from masks.
Both Liu~\etal~\cite{liu2018partialinpainting} and Yu~\etal~\cite{yu2018free} adopt PatchGAN discriminator~\cite{pix2pix2017} to improve the coherence further.
Recently, several models were proposed to significantly improve the image painting quality by incorporating the structure knowledge in a different context, including image edges~\cite{nazeri2019edgeconnect,Li_2019_ICCV}, object contours~\cite{Xiong_2019_CVPR}, smooth edge-preserving map~\cite{ren2019structureflow}, and gradient map~\cite{jie2020inpainting}.
Nazeri~\etal~\cite{nazeri2019edgeconnect} introduced a two-stage network named EdgeConnect, which firstly recovers the missing edges in the masked regions, followed by a generator conditioned on the reconstructed edge map.
The authors prove that the structure-to-content approach can effectively preserve the structure information in the inpainting results. However, EdgeConnect uses canny edges to represent structure features, which might be suitable for natural images but may lead to complex local edges in indoor scenes. In contrast, our work exploits the HorizonNet~\cite{SunHSC19} to estimate layout edges, representing the global room structure, which is suitable for our indoor inpainting task. In addition, our model is an end-to-end architecture instead of a two-stage network.
Yang~\etal~\cite{jie2020inpainting} developed a multi-task learning framework to jointly learn the completion of image contents and structure map (edges and gradient).
A structure embedding scheme is employed to embed the learned structure features while inpainting explicitly.
The model further learns to exploit the recurrent structures and contents via an attention mechanism.
While demonstrating impressive performance in generating realistic results, these structure-aware methods still fail to model long-range structure correspondence such as the layout in the indoor scenes.
On the other hand, some works have successfully recovered a single partially occluded object~\cite{8578741,9710417}. However, their architecture does not handle multiple object instances of the same class and is thus not suitable for our context where the plane-wise segmentation consists of different numbers of wall planes.
\input{Figures/fig_arc_overview}

\heading{Image-to-image translation.}
The image inpainting is essentially a constrained image-to-image translation problem.
Significant efforts have been made to tackle various problems based on image-to-image translation architectures~\cite{pix2pix2017,CycleGAN2017,karras2019stylebased}.
Here we focus on the ones that are closely related to our work.
Park~\etal~\cite{park2019SPADE} introduced SPADE, which utilizes a spatial adaptive normalization layer for synthesizing photorealistic images given an input semantic segmentation map.
Specifically, a spatially-adaptive learned transform modulates the activation layer with a semantic segmentation map and effectively propagates the semantic information throughout the network.
In contrast to SPADE, which uses only one style code to control the image synthesis, Zhu~\etal~\cite{Zhu_2020_CVPR} presents SEAN by extending the SPADE architecture with per-region style encoding.
By embedding one style code for individual semantic classes, SEAN shows significant improvement over SPADE and generates the highest quality results.
In the context of indoor scenes, Gkitsas~\etal~\cite{gkitsas2021panodr} introduce PanoDR that combines image-to-image translation with a generator to constrain the image inpainting with the underlying scene structure.
Percisely, to convert a furnished indoor panorama into its empty counterpart, PanoDR exploits a generator for synthesizing photorealistic image contents where the global layout structure is preserved via an image-to-image translation module.
The empty indoor panorama is then used to complete the masked regions in the input panorama via a simple copy-and-paste process.
Gkitsas~\etal~\cite{DBLP:conf/visapp/GkitsasZSDZ22} extend the architecture of PanoDR to make the model end-to-end trainable. However, the quantitative evaluation indicates that the performance improvement is marginal compared with PanoDR.
Our system also combines a generator with image-to-image translation as PanoDR does. However, we obtain superior results than PanoDR by exploiting the global layout edges as a prior and adapting SEAN blocks in a local plane-wise manner to guide the inpainting. Moreover, in contrast to PanoDR performs the
inpainting task via an indirect way, our system performs the inpainting task in an end-to-end fashion, directly completing the mask areas instead of hallucinating an empty scene, thus resulting in better visual quality and consistency.
\section{Discussions and Future Work}
\section{Conclusions}
\label{sec:conclusions}
We proposed an end-to-end structural inpainting network for the indoor scene.
We introduce layout boundary line conditions the output structure and utilize the plane-aware normalization to enhance planar style consistency.
Experiment results show the outstanding performance of our model in both structural inpainting and furniture removal on the indoor scene.

\heading{Limitations.}
In the real-world application of furniture removal, we can often see residuals of shading effect caused by the removed furniture.
These residuals are hard to segment and even harder to model.
As shown in~\figref{Limitation}, our model is slightly affected by these residuals but still produces more realistic results than PanoDR~\cite{gkitsas2021panodr}.

\heading{Future work.}
We plan to adopt a more reasonable segmentation mask of the indoor scene inpainting which can cover the shading area and thus improve our results in those shaded scenes.
\input{Figures/fig_comp_limitation}

\heading{Acknowledgements.} The project was funded in part by the National Science and Technology Council of Taiwan (110-2221-E-007-060-MY3, 110-2221-E-007-061-MY3).

\section{Method}
\label{sec:method}
\input{Figures/fig_arc_pa_normalization}

\subsection{Layout Guidance Map}
\label{sec:layout_guidance}
We employ an off-the-shelf model, HorizonNet~\cite{SunHSC19}, to estimate a layout map from input masked panorama.
Through a recurrent neural network, the HorizonNet predicts a 3-dimensional vector representing ceiling, ground, and corner location.
We further process the output vector to generate a layout map ${\mathbf{L}_{m}}$ comprising low-level boundary lines.
This layout map serves as a conditional input to encourage the preservation of global layout structure while inpainting.
Moreover, we extract two semantic segmentation maps from the layout map that depict
(i) the segmentation mask ${\mathbf{L}_{3-class}}$ with three semantic labels of indoor scene, \ie ceiling, floor, and wall; and
(ii) a plane-wise segmentation mask ${\mathbf{L}_{p-wise}}$ where pixels are indexed in a per structural plane basis (\ie ceiling, floor, or individual walls).
These semantic segmentation maps are generated using conventional image processing operations (i.e, flood-fill) and will be used in the later normalization module.

\subsection{Image Inpainting Backbone}
\label{sec:inpainting_backbone}
As shown in~\figref{overview}, our network architecture consists of one generator and one discriminator.
The generator $\mathbf{G}$ follows a conventional scheme with one content encoder and one image decoder.
The content encoder consists of two down-sampling convolution blocks followed by eight residual blocks using dilated convolution~\cite{7780459}.
The image decoder uses a cascade of our proposed plane-aware residual blocks and two up-sampling blocks.
Motivated by EdgeConnect~\cite{nazeri2019edgeconnect}, we use PatchGAN~\cite{isola2018imagetoimage} as our discriminator to determine the real or fake sample by dividing the input image into several patches.
In the following sections, we will elaborate plane-aware residual block, loss functions, and discriminator in more detail.

\subsection{Plane-aware Normalization}
\label{sec:planar-aware_module}
Considering the different styles among wall planes is very common in real-world indoor scenes.
We follow the architecture of SEAN~\cite{Zhu_2020_CVPR} and propose leveraging two kinds of segmentation maps ${\mathbf{L}_{p-wise}}$ and ${\mathbf{L}_{3-class}}$ to establish our plane-aware normalization (see~\figref{pa_normal}).
Our plane-aware normalization consists of one style encoder and two style blocks, which enhance the global style semantics and local style consistency of the generated results.
The inputs of the style encoder include masked panoramic image ${\mathbf{I}}_{in}$ and mask image $\mathbf{M}$.
We use partial convolution blocks in style encoder instead of vanilla convolution to make feature extraction conditioned only valid pixels.
We first adopt the plane-wise average pooling on the output features to generate style codes for each plane based on ${\mathbf{L}_{p-wise}}$.
Second, we spatially broadcast each style code on the corresponding area and output the local style block.
On the other side, we predict the global style block by passing the 3-class segmentation map ${\mathbf{L}_{3-class}}$ through several convolution layers.
Finally, the remaining part of our plane-aware normalization follows the same architecture of SEAN~\cite{Zhu_2020_CVPR}, and combines global and local style blocks into the downstream $\beta$ and $\gamma$ parameters of the final batch normalization.

\subsection{Loss Functions}
\label{sec:loss_func}
Here we elaborate on the low- and high-level loss functions and the discrimination used for training our image generator.

\heading{Reconstruction loss} measures the low-level pixel-based loss between the predicted and ground-truth images.
To encourage the generator to pay more attention to the missing regions, we additionally calculate the $L_{1}$ loss in the missing regions. The reconstruction loss $L_{rec}$ is defined as follows:

\begin{equation}
L_{rec}=\left\|\mathbf{M}\odot {\mathbf{I}}_{gt} - \mathbf{M}\odot {\mathbf{I}}_{out} \right\|_1+\left\|{\mathbf{I}}_{gt} - {\mathbf{I}}_{out} \right\|_1,
\label{eqn:rec_loss}
\end{equation}
where ${\mathbf{I}}_{gt}$ and ${\mathbf{I}}_{out}$ represent the ground-truth image and the generator's output, respectively, and $\mathbf{M}$ is a binary mask.

\heading{Perceptual loss} encourages the predicted and ground-truth images to have similar representation in high-level feature space extracted via a pre-trained VGG-19~\cite{simonyan2015deep}, and is defined as follows:
\begin{equation}
L_{perc}=\sum_{i} \left\|\phi_{i}\left({\mathbf{I}}_{gt}\right)-\phi_{i}\left({\mathbf{I}}_{out}\right)\right\|_{1},
\label{eqn:perc_loss}
\end{equation}
where $\phi_{i}$ is the activation map of the $ith$ layer of the pre-trained feature extraction network.

\heading{Style loss} calculates the co-variance difference between the activation maps. For the activation map $\phi_{i}$ of size $C_{i}$ × $H_{i}$ × $W_{i}$, the style loss is defined as follows:
\begin{equation}
L_{sty}=\left\|G_{i}^{\phi}\left({\mathbf{I}}_{gt}\right)-G_{i}^{\phi}\left({\mathbf{I}}_{out}\right)\right\|_{1},
\label{eqn:sty_loss}
\end{equation}
where $G_{i}^{\phi}$ is a $C_{i}$ × $C_{i}$ gram matrix~\cite{Gatys_2016_CVPR} constructed by the activation map $\phi_{i}$.

\heading{Adversarial loss} is implemented with the patch-based discriminator~\cite{isola2018imagetoimage}, which outputs the feature map divided into several feature patches and uses hinge loss~\cite{lim2017geometric} to optimize the generator $G$ and the discriminator $D$.
The adversarial loss for generator $G$ and discriminator $D$ are defined as follows:
\begin{equation}
L_G=-D\left({\mathbf{I}}_{out} \right),
\label{eqn:adv_loss}
\end{equation}
\begin{equation}
L_D=\lambda_{D}\left(max\left(0,1+D \left({\mathbf{I}}_{out} \right)\right) + max\left(0,1-D \left({\mathbf{I}}_{gt} \right)\right)\right);
\label{eqn:d_loss}
\end{equation}
The overall loss function used in the generator $G$ is defined as follows:
\begin{equation}
L_{total}=\lambda_{rec} L_{rec}+\lambda_{perc} L_{perc}+\lambda_{sty} L_{sty}+\lambda_{G} L_G,
\label{eqn:total_loss}
\end{equation}
where $\lambda_{rec}$, $\lambda_{perc}$, $\lambda_{sty}$, $\lambda_{G}$, and $\lambda_{D}$ are the hyperparameters for weighting the loss functions.

\section{Introduction}
\label{sec:intro}
Image inpainting is a widely investigated topic in computer graphics and vision communities, which aims at filling in missing regions of an image with photorealistic and fine detailed content.
It plays a crucial step toward many practical applications, such as image restoration, object removal, \etc.
With the rapid development of deep learning, image inpainting has been revisited and improved significantly in the past few years.
A considerable body of researches has been explored to generate impressive results on perspective datasets.

In this work, we address the image inpainting problem in the context of indoor panoramas.
Indoor panoramas provide excellent media for the holistic scene understanding~\cite{LNCS86940668panocontext} that would further benefit several applications such as object detection, depth estimation, furniture rearrangement, \etc.
In particular, removing foreground objects and filling the missing regions in an indoor panorama is essential for the interior redesign task.
However, the complex structures and textures presented in the indoor scenes make the inpainting problem non-trivial and challenging for previous methods.
As shown in~\figref{FR_SI}(EC), results generated by a state-of-the-art deep learning method fail to align the image structure along the layout boundaries and produce inconsistent blurry image contents.

Recently, Gkitsas~\etal~\cite{gkitsas2021panodr} introduced PanoDR, a diminished reality-oriented inpainting model for indoor panorama.
The main idea is to translate a furnished indoor panorama into its empty counterpart via a network that leverages both a generator and an image-to-image translation module.
The inpainting result is then obtained by compositing the predicted empty panorama and input panorama using the object mask.
However, there are still obvious artifacts near the boundaries of masked regions as shown in~\figref{FR_SI}.
\input{Figures/Figure_1_ver2}

To achieve better inpainting quality, we present an end-to-end deep generative adversarial framework that exploits both the global and local context of indoor panoramas to guide the inpainting process.
Specifically, we take the low-level layout boundaries estimated from input panorama as a conditional input to guide the inpainting model, encouraging the preservation of sharp boundaries in the filled image.
A plane-aware normalization module is then employed to embed local plane-wise style features derived from the layout into the image decoder, encouraging local texture restoration from adjacent room structures (\ie ceiling, floor, and individual walls).
We train and evaluate our model on a public indoor panorama dataset, Structured3D~\cite{Structured3D}.
Experimental results show that our method produces results superior to several state-of-the-art methods (see~\figref{teaser}, \figref{FR_SI} and \figref{comp_sota_si_fr}). The main contributions are summarized as follows:
\begin{itemize}
\item We present an end-to-end generative adversarial network that incorporates both the global and local context of indoor panoramas to guide the inpainting process.
\item We introduce a plane-aware normalization module that guides the image decoder with spatially varying normalization parameters per structural plane (\ie ceiling, floor, and individual walls).
\item Our method achieves state-of-the-art performance and visual quality on synthetic and real-world datasets.
\end{itemize}
\section{Related Work}
\label{sec:related}

\heading{Traditional image inpainting.}
There are two main genres among traditional image inpainting works: diffusion-based methods and patch-based methods.
Diffusion-based methods~\cite{mumford,10.1145/364338.364405,935036,Drori2003FragmentbasedIC,Liang2015AnEF,wei2016} propagate pixels from neighboring regions to the missing ones to synthesize the image content.
On the other hand, patch-based methods~\cite{patchmatch,6960838,7180400,7922581,10.1007/s11042-017-4509-0,8352516,10.1007/s11042-017-5077-z} fill the missing regions by searching for and copying similar image patches from the rest of the image or existing image datasets.
Without a high-level understanding of the image contents, these methods easily fail on images with complex structures.

\heading{Learning-based image inpainting.}
With the rapid development of deep learning, several image inpainting techniques based on convolutional neural networks (CNN) have been proposed.
These methods aim to learn a generator from a large dataset to produce photorealistic image contents in the missing regions effectively.
Context Encoders~\cite{pathakCVPR16context} pioneers CNN-based image inpainting by proposing an adversarial network with an encoder-decoder architecture.
However, due to the information bottleneck layer of the autoencoder, the results are often blurry, incoherent, and can not work on irregular masks.
Yu~\etal~\cite{yu2018generative} proposed a coarse-to-fine network and a context-aware mechanism to reduce blurriness.
Iizuka~\etal~\cite{IizukaSIGGRAPH2017} adopted local and global discriminators and used dilated convolutions to increase the model's receptive field and enhance coherence.
Liu~\etal~\cite{liu2018partialinpainting} proposed partial convolutions, which only consider valid pixels during convolution, to handle irregular masks.
Yu~\etal~\cite{yu2018free} further extends the partial convolutions by introducing a dynamic feature gating mechanism, named gated convolutions, to deal with free-from masks.
Both Liu~\etal~\cite{liu2018partialinpainting} and Yu~\etal~\cite{yu2018free} adopt PatchGAN discriminator~\cite{pix2pix2017} to improve the coherence further.
Recently, several models were proposed to significantly improve the image painting quality by incorporating the structure knowledge in a different context, including image edges~\cite{nazeri2019edgeconnect,Li_2019_ICCV}, object contours~\cite{Xiong_2019_CVPR}, smooth edge-preserving map~\cite{ren2019structureflow}, and gradient map~\cite{jie2020inpainting}.
Nazeri~\etal~\cite{nazeri2019edgeconnect} introduced a two-stage network named EdgeConnect, which firstly recovers the missing edges in the masked regions, followed by a generator conditioned on the reconstructed edge map.
The authors prove that the structure-to-content approach can effectively preserve the structure information in the inpainting results. However, EdgeConnect uses canny edges to represent structure features, which might be suitable for natural images but may lead to complex local edges in indoor scenes. In contrast, our work exploits the HorizonNet~\cite{SunHSC19} to estimate layout edges, representing the global room structure, which is suitable for our indoor inpainting task. In addition, our model is an end-to-end architecture instead of a two-stage network.
Yang~\etal~\cite{jie2020inpainting} developed a multi-task learning framework to jointly learn the completion of image contents and structure map (edges and gradient).
A structure embedding scheme is employed to embed the learned structure features while inpainting explicitly.
The model further learns to exploit the recurrent structures and contents via an attention mechanism.
While demonstrating impressive performance in generating realistic results, these structure-aware methods still fail to model long-range structure correspondence such as the layout in the indoor scenes.
On the other hand, some works have successfully recovered a single partially occluded object~\cite{8578741,9710417}. However, their architecture does not handle multiple object instances of the same class and is thus not suitable for our context where the plane-wise segmentation consists of different numbers of wall planes.
\input{Figures/fig_arc_overview}

\heading{Image-to-image translation.}
The image inpainting is essentially a constrained image-to-image translation problem.
Significant efforts have been made to tackle various problems based on image-to-image translation architectures~\cite{pix2pix2017,CycleGAN2017,karras2019stylebased}.
Here we focus on the ones that are closely related to our work.
Park~\etal~\cite{park2019SPADE} introduced SPADE, which utilizes a spatial adaptive normalization layer for synthesizing photorealistic images given an input semantic segmentation map.
Specifically, a spatially-adaptive learned transform modulates the activation layer with a semantic segmentation map and effectively propagates the semantic information throughout the network.
In contrast to SPADE, which uses only one style code to control the image synthesis, Zhu~\etal~\cite{Zhu_2020_CVPR} presents SEAN by extending the SPADE architecture with per-region style encoding.
By embedding one style code for individual semantic classes, SEAN shows significant improvement over SPADE and generates the highest quality results.
In the context of indoor scenes, Gkitsas~\etal~\cite{gkitsas2021panodr} introduce PanoDR that combines image-to-image translation with a generator to constrain the image inpainting with the underlying scene structure.
Percisely, to convert a furnished indoor panorama into its empty counterpart, PanoDR exploits a generator for synthesizing photorealistic image contents where the global layout structure is preserved via an image-to-image translation module.
The empty indoor panorama is then used to complete the masked regions in the input panorama via a simple copy-and-paste process.
Gkitsas~\etal~\cite{DBLP:conf/visapp/GkitsasZSDZ22} extend the architecture of PanoDR to make the model end-to-end trainable. However, the quantitative evaluation indicates that the performance improvement is marginal compared with PanoDR.
Our system also combines a generator with image-to-image translation as PanoDR does. However, we obtain superior results than PanoDR by exploiting the global layout edges as a prior and adapting SEAN blocks in a local plane-wise manner to guide the inpainting. Moreover, in contrast to PanoDR performs the
inpainting task via an indirect way, our system performs the inpainting task in an end-to-end fashion, directly completing the mask areas instead of hallucinating an empty scene, thus resulting in better visual quality and consistency.
\section{Overview}
\label{sec:overview}

\figref{overview} illustrates an overview of our architecture.
Our system takes a masked panoramic image ${\mathbf{I}}_{in}$ and the corresponding binary mask $\mathbf{M}$ as inputs and generates the inpainted panoramic image ${\mathbf{I}}_{out}$.
The masked panoramic image is generated by ${{\mathbf{I}}_{in} = {\mathbf{I}}_{gt} \odot(\mathbf{1}-\mathbf{M})}$, where ${\mathbf{I}}_{gt}$ represents the ground-truth panoramic image and $\odot$ denotes the Hadamard product.
Our system first utilizes an off-the-shelf model to estimate the room layout ${\mathbf{L}_{m}}$ from input masked panoramic image.
This layout map is then concatenated with ${\mathbf{I}}_{in}$ and $\mathbf{M}$ to obtain a five-channel input map fed into the generator $\mathbf{G}$.
We further derive two semantic segmentation maps ${\mathbf{L}_{3-class}}$ and ${\mathbf{L}_{p-wise}}$ using the layout map for the subsequent normalization module (\secref{layout_guidance}).
The image generation model follows the conventional generative adversarial architecture with one content encoder and one image decoder with one discriminator. (\secref{inpainting_backbone}).
To impose structure information during inpainting, we introduce a plane-aware normalization that modifies the SEAN~\cite{Zhu_2020_CVPR} block with two semantic segmentation maps to guide the decoder with spatially varying normalization parameters per structural plane (\ie ceiling, floor, and individual walls).
Such a plane-aware normalization provides useful guidance for global structure preservation as well as consistent local image content generation (\secref{planar-aware_module}).
Finally, common loss functions in image inpainting, including the reconstruction loss, the perceptual loss, the style loss, and the adversarial loss are employed to train our model (\secref{loss_func}).
\section{Method}
\label{sec:method}
\input{Figures/fig_arc_pa_normalization}

\subsection{Layout Guidance Map}
\label{sec:layout_guidance}
We employ an off-the-shelf model, HorizonNet~\cite{SunHSC19}, to estimate a layout map from input masked panorama.
Through a recurrent neural network, the HorizonNet predicts a 3-dimensional vector representing ceiling, ground, and corner location.
We further process the output vector to generate a layout map ${\mathbf{L}_{m}}$ comprising low-level boundary lines.
This layout map serves as a conditional input to encourage the preservation of global layout structure while inpainting.
Moreover, we extract two semantic segmentation maps from the layout map that depict
(i) the segmentation mask ${\mathbf{L}_{3-class}}$ with three semantic labels of indoor scene, \ie ceiling, floor, and wall; and
(ii) a plane-wise segmentation mask ${\mathbf{L}_{p-wise}}$ where pixels are indexed in a per structural plane basis (\ie ceiling, floor, or individual walls).
These semantic segmentation maps are generated using conventional image processing operations (i.e, flood-fill) and will be used in the later normalization module.

\subsection{Image Inpainting Backbone}
\label{sec:inpainting_backbone}
As shown in~\figref{overview}, our network architecture consists of one generator and one discriminator.
The generator $\mathbf{G}$ follows a conventional scheme with one content encoder and one image decoder.
The content encoder consists of two down-sampling convolution blocks followed by eight residual blocks using dilated convolution~\cite{7780459}.
The image decoder uses a cascade of our proposed plane-aware residual blocks and two up-sampling blocks.
Motivated by EdgeConnect~\cite{nazeri2019edgeconnect}, we use PatchGAN~\cite{isola2018imagetoimage} as our discriminator to determine the real or fake sample by dividing the input image into several patches.
In the following sections, we will elaborate plane-aware residual block, loss functions, and discriminator in more detail.

\subsection{Plane-aware Normalization}
\label{sec:planar-aware_module}
Considering the different styles among wall planes is very common in real-world indoor scenes.
We follow the architecture of SEAN~\cite{Zhu_2020_CVPR} and propose leveraging two kinds of segmentation maps ${\mathbf{L}_{p-wise}}$ and ${\mathbf{L}_{3-class}}$ to establish our plane-aware normalization (see~\figref{pa_normal}).
Our plane-aware normalization consists of one style encoder and two style blocks, which enhance the global style semantics and local style consistency of the generated results.
The inputs of the style encoder include masked panoramic image ${\mathbf{I}}_{in}$ and mask image $\mathbf{M}$.
We use partial convolution blocks in style encoder instead of vanilla convolution to make feature extraction conditioned only valid pixels.
We first adopt the plane-wise average pooling on the output features to generate style codes for each plane based on ${\mathbf{L}_{p-wise}}$.
Second, we spatially broadcast each style code on the corresponding area and output the local style block.
On the other side, we predict the global style block by passing the 3-class segmentation map ${\mathbf{L}_{3-class}}$ through several convolution layers.
Finally, the remaining part of our plane-aware normalization follows the same architecture of SEAN~\cite{Zhu_2020_CVPR}, and combines global and local style blocks into the downstream $\beta$ and $\gamma$ parameters of the final batch normalization.

\subsection{Loss Functions}
\label{sec:loss_func}
Here we elaborate on the low- and high-level loss functions and the discrimination used for training our image generator.

\heading{Reconstruction loss} measures the low-level pixel-based loss between the predicted and ground-truth images.
To encourage the generator to pay more attention to the missing regions, we additionally calculate the $L_{1}$ loss in the missing regions. The reconstruction loss $L_{rec}$ is defined as follows:

\begin{equation}
L_{rec}=\left\|\mathbf{M}\odot {\mathbf{I}}_{gt} - \mathbf{M}\odot {\mathbf{I}}_{out} \right\|_1+\left\|{\mathbf{I}}_{gt} - {\mathbf{I}}_{out} \right\|_1,
\label{eqn:rec_loss}
\end{equation}
where ${\mathbf{I}}_{gt}$ and ${\mathbf{I}}_{out}$ represent the ground-truth image and the generator's output, respectively, and $\mathbf{M}$ is a binary mask.

\heading{Perceptual loss} encourages the predicted and ground-truth images to have similar representation in high-level feature space extracted via a pre-trained VGG-19~\cite{simonyan2015deep}, and is defined as follows:
\begin{equation}
L_{perc}=\sum_{i} \left\|\phi_{i}\left({\mathbf{I}}_{gt}\right)-\phi_{i}\left({\mathbf{I}}_{out}\right)\right\|_{1},
\label{eqn:perc_loss}
\end{equation}
where $\phi_{i}$ is the activation map of the $ith$ layer of the pre-trained feature extraction network.

\heading{Style loss} calculates the co-variance difference between the activation maps. For the activation map $\phi_{i}$ of size $C_{i}$ × $H_{i}$ × $W_{i}$, the style loss is defined as follows:
\begin{equation}
L_{sty}=\left\|G_{i}^{\phi}\left({\mathbf{I}}_{gt}\right)-G_{i}^{\phi}\left({\mathbf{I}}_{out}\right)\right\|_{1},
\label{eqn:sty_loss}
\end{equation}
where $G_{i}^{\phi}$ is a $C_{i}$ × $C_{i}$ gram matrix~\cite{Gatys_2016_CVPR} constructed by the activation map $\phi_{i}$.

\heading{Adversarial loss} is implemented with the patch-based discriminator~\cite{isola2018imagetoimage}, which outputs the feature map divided into several feature patches and uses hinge loss~\cite{lim2017geometric} to optimize the generator $G$ and the discriminator $D$.
The adversarial loss for generator $G$ and discriminator $D$ are defined as follows:
\begin{equation}
L_G=-D\left({\mathbf{I}}_{out} \right),
\label{eqn:adv_loss}
\end{equation}
\begin{equation}
L_D=\lambda_{D}\left(max\left(0,1+D \left({\mathbf{I}}_{out} \right)\right) + max\left(0,1-D \left({\mathbf{I}}_{gt} \right)\right)\right);
\label{eqn:d_loss}
\end{equation}
The overall loss function used in the generator $G$ is defined as follows:
\begin{equation}
L_{total}=\lambda_{rec} L_{rec}+\lambda_{perc} L_{perc}+\lambda_{sty} L_{sty}+\lambda_{G} L_G,
\label{eqn:total_loss}
\end{equation}
where $\lambda_{rec}$, $\lambda_{perc}$, $\lambda_{sty}$, $\lambda_{G}$, and $\lambda_{D}$ are the hyperparameters for weighting the loss functions.
\section{Experiments}
\label{sec:results}
In this section,  we evaluate the performance of our model by comparing it with several state-of-the-art image inpainting approaches and conducting ablation studies to verify the necessity of individual components in the proposed architecture.
Please refer to our online webpage for other experiments and more results\footnote{\url{https://ericsujw.github.io/LGPN-net/}}.

\subsection{Experimental Settings}
\heading{Dataset and baselines.}
We compare our model with the following state-of-the-art structure-aware image inpainting models:
\begin{itemize}
    \item EC~\cite{nazeri2019edgeconnect}: a two-stage adversarial network that comprises an edge completion model followed by a generator.

    \item LISK~\cite{jie2020inpainting}: a multi-task learning framework that exploits image structure embedding and an attention mechanism in the generator.

    \item PanoDR~\cite{gkitsas2021panodr}: a deep learning framework that combines image-to-image translation with generator to condition the inpainting on the indoor scene structure.
\end{itemize}
The experiments were conducted on a public indoor panorama dataset, Structured3D~\cite{Structured3D}, which contains 21,835 indoor panoramas.
The official data split is adopted for training(18,362), validation(1,776), and testing(1,697).
We follow the same procedure as PanoDR to generate mask images using contours of foreground furniture (see Section 3.1).
We use the officially released implementation of baselines for training from scratch and testing.
Note that each indoor panorama in Structured3D has two representations of the same scene (\ie empty and furnished).
Therefore, the experiments were conducted in two phases to evaluate our model and baselines in different application scenarios (\ie structural inpainting vs. furniture removal).

\heading{Evaluation metrics.}
We take several commonly used image quality assessment metrics in previous inpainting approaches for quantitative evaluation.
Specifically, we used the low-level feature metrics, including Mean Absolute Error (MAE), Peak Signal-to-Noise (PSNR), Structural Similarity Index (SSIM)~\cite{1284395},
and Fréchet Inception Distance (FID)~\cite{heusel2018gans}.

\heading{Implementation details.}
We implement our model in PyTorch and conduct the experiments on a single NVIDIA V100 with 32G VRAM.
The resolution of the panoramic images is resized to $512 \times 256$.
We use Adam~\cite{kingma2017adam} optimizer in the training process with the hyper-parameters setting of $b_{1} = 0.0$ and $b_{2} = 0.9$, a learning rate of $0.0001$, and a batch size of 8.
We empirically set $\lambda_{rec} = 1$, $\lambda_{perc} = 0.1$, $\lambda_{sty} = 250$, $\lambda_{G} = 0.1$, and $\lambda_{D} = 0.5$ in the total loss function (\eqnref{total_loss}).
For HorizonNet~\cite{SunHSC19}, we use the official pre-trained model for layout estimation.

\subsection{Evaluation on the Empty Scenes}
In this experiment, we evaluate both the qualitative and quantitative performance of our model on the image inpainting task by comparing it with baselines.
The qualitative comparisons are shown in~\figref{comp_sota_si_fr} (top 8 rows).
In contrast to EC and LISK, which fail to restore image structures in the masked regions, our method faithfully generates image contents adhering to the underlying layout structure.
While PanoDR shows slightly better structure preservation than EC and LISK, it fails to generate image contents consistent with the surrounding of masked regions as our method does.
Therefore, our method achieves the best performance against all the baselines across all evaluation metrics as shown in~\tabref{result_comp} (top).

\subsection{Evaluation on the Furnished Scenes}
Furniture of irregular shape will more or less obscure the layout of the indoor scene, making it more challenging to restore the regular structure in the missing area.
Therefore, in this experiment, we would like to evaluate how well our model learned from empty scenes can generalize to the furnished scenes.
Since the inpainting task setup here exactly matches the one defined in the PanoDR, we use the pre-trained model of PanoDR in this experiment for a fair comparison.
As shown in~\figref{comp_sota_si_fr} (bottom 8 rows), our method still clearly outperforms baselines in generating image contents that align the layout structure well and are consistent with the surrounding of the masked regions.
The quantitative results are shown in~\tabref{result_comp} (bottom).
It is worth noting that the way PanoDR performs image completion via compositing the predicted empty image and input image using the object mask will lead to severe artifacts where occlusion occurred between foreground objects (see~\figref{FR_SI}(PanoDR)).
\input{Figures/tbl_empty_full}

\subsection{Ablation Study}
Here, we conduct ablation studies to validate our model from different perspectives.
First, we evaluate the necessity of individual design choices in our architecture.
Then, we conduct two experiments to evaluate how sensitive our model is to the size of input masks and the quality of input layout maps.
\input{Figures/fig_ablation_new}
\input{Figures/tbl_ablation}

\heading{Ablation on network architecture.}
In this experiment, we start with the backbone model ($\mathbf{Backbone}$) as the baseline, then progressively adding only layout guidance map (${\mathbf{Layout\ map\ only}}$), and our plane-aware normalization ($\mathbf{Full\ model}$).
As shown in~\tabref{ablation_study}, we obtain the best performance with the full model on all the metrics.
The qualitative comparisons shown in~\figref{ablation_new} indicate that adding layout guidance map generates clear structure boundaries in the final result ($2^{nd}$ and $3^{rd}$ columns), while our full model with plane-aware normalization can constrain the image generation to the adjacent structural planes and obtain visually consistent results ($3^{rd}$ and $4^{th}$ columns).

\heading{Sensitivity to the mask size.}
In this experiment, we analyze the testing dataset and classify the images into different categories according to the area proportions of input masks. \tabref{add_exp1} shows the inpainting performance for each category. We can tell that the inpainting quality degrades with the increasing mask size. A significant drop occurs where the ratio of input mask is greater than 30{\%}.

\heading{Sensitivity to the layout estimation.}
In order to explore the effect of the accuracy of layout estimation on the inpainting quality, we first devise a mechanism to generate layout maps with different levels of accuracy.
Specifically, we feed masked images of different mask sizes into HorizonNet. We start by generating randomly located rectangle masks of 5\% image size and increase the mask ratio to 10\%, 30\% and 50\% to deliberately produce layout structures with decreasing quality.
Then we take these layout maps as conditional inputs of our model and compare the inpainting performance empty-room testing dataset.
As shown in~\tabref{add_exp2}, our model degrades marginally when the quality of estimated layouts decreases from 0.96 to 0.84, indicating our model is robust to the varying input layout maps.
\input{Figures/tbl_addtion_mask}
\input{Figures/tbl_addtion_layout}
\input{Figures/fig_real_scene}

\subsection{Qualitative Results on Real-world Scene}
Real-world scenes have complex lighting and layout structure.
However, the amount of data in the real-world scene dataset and the quality of furniture category annotations are insufficient for training our model, so we choose to train on the synthetic dataset Structured3D~\cite{Structured3D}.
Nevertheless, we still compare our results with PanoDR~\cite{gkitsas2021panodr}, which also implements the furniture removal task, on the real-world scene dataset.
Since the real-world scene dataset does not contain paired data (\ie scenes before and after furniture removal), quantitative evaluation is infeasible and we can only provide qualitative comparisons here.
\figref{real_scene} shows that our inpainted results have a higher quality of structural maintenance and color restoration. Moreover, compared with PanoDR, we can still exert more stable performance in real-world scenes.
Please refer to our online webpage for more results\footnote{\url{https://ericsujw.github.io/LGPN-net/}}.
\section{Discussions and Future Work}
\section{Conclusions}
\label{sec:conclusions}
We proposed an end-to-end structural inpainting network for the indoor scene.
We introduce layout boundary line conditions the output structure and utilize the plane-aware normalization to enhance planar style consistency.
Experiment results show the outstanding performance of our model in both structural inpainting and furniture removal on the indoor scene.

\heading{Limitations.}
In the real-world application of furniture removal, we can often see residuals of shading effect caused by the removed furniture.
These residuals are hard to segment and even harder to model.
As shown in~\figref{Limitation}, our model is slightly affected by these residuals but still produces more realistic results than PanoDR~\cite{gkitsas2021panodr}.

\heading{Future work.}
We plan to adopt a more reasonable segmentation mask of the indoor scene inpainting which can cover the shading area and thus improve our results in those shaded scenes.
\input{Figures/fig_comp_limitation}

\heading{Acknowledgements.} The project was funded in part by the National Science and Technology Council of Taiwan (110-2221-E-007-060-MY3, 110-2221-E-007-061-MY3).

redpajama_set_name: RedPajamaArXiv


Ten billion died. 3 million are left clawing for survival. yet another dying doesn't make a distinction, does it?
Micka is just 16 while she makes a decision to kill herself. She doesn't slot in, doesn't comprehend humans or principles of society. She doesn't even comprehend herself. To her, all phrases have flavors, and feelings include such strength that she can't support yet carve them into her skin.
Only hours prior to her meticulously deliberate suicide, she meets Runner - a mysterious stranger who makes a proposal she reveals demanding to reject: a lifestyles far-off from domestic as a Sequencer's apprentice.
'Set in a vividly realised global of elite Alphas and their 'weaker' Omega twins, it holds a reflect as much as our obsession with perfection' GuardianThey have been born jointly and they'll die jointly. One powerful Alpha dual and one mutated Omega; the single factor they percentage is the instant in their demise. The Omegas stay in segregation, solid out by means of their households once their mutation turns into transparent.
The remarkable Invasion is a unprecedented political anticipation brief tale written via Jack London and primary released in McClure's in July 1910 and later within the booklet The power of the robust (New York, Macmillan, 1914). the tale starts off in 1910s China. less than the impact of Japan, China modernizes and has its personal Meiji Reforms.


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The music is largely post-Ornette post-bop; Antunes is a powerful bassist, as he's gotta be if he's gonna be the only chordal instrument in the whole ensemble. He pulls the strings like a young Charles Mingus; there are multiple passages during which echoes of "Haitian Fight Song" or "II B.S." seem to drift through. At other times, he strums the bass like a huge guitar, the way Jimmy Garrison used to behind John Coltrane. Meanwhile, the two saxophonists play not just simultaneously, but together, working their way through intricate melody lines and conversing on the fly. The music occasionally drifts into ultra-free improv that sounds like it should have a capital I, but things always wind up back where they belong, in the realm of muscular, swinging jazz. Lobo and Patrman hit hard when that's what's called for, and play off each other very well at all times. Their rhythmic dance is easily the most interesting part of many moments here.
There's not a whole lot to say about an album like this. Strong compositions, well played by a sympathetic and talented ensemble that, despite being assembled for the date (from multiple countries), comes together with a surety and a sense of common purpose that's just wildly enjoyable to hear. It would be a very good thing indeed if this ensemble continued to work together in the future, both live and in the studio.


He wanted to watch a horror film to my dismay. In all honesty, lagi nyang pinu-push ang horror everytime we're at the cinema na sya naman kinakabadtrip ko! Tigas ng skull eh! Sabi ng I don't have the heart for horror films.
Anyway, I dragged him to Percy Jackson and The Sea of Monsters just because I wanna watch the film. I saw the first installment and actually liked it so I'm looking forward for the 2nd installment.
So how's the movie you say? It was fun. I love greek mythology so the mere mention of Zeus, Hermes, Odysseus, Hades and Poseidon is enough to catch my attention. Like the first film, the effects are great and the storyline is just as captivating. I love Logan Lerman. He's a sweetheart and his portrayal of Percy Jackson is just superb.
I love the action scenes and I think this movie is a great teaser for the next film. Now, I wanna know what happens now that Thalia, daughter of Zeus is alive. Nyahahaha!
Oh! And bring me those Percy Jackson books please. I wanna read it!!!


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text:
Autism and the MMR: Finally a Retraction
Feb 5, 2010 • 3
By NAOMI FREUNDLICH
Are we finally ready to close the door on the much-disputed link between the MMR vaccine and autism?
On January 30, Britain's General Medical Council ruled that Andrew Wakefield, a gastroenterologist, had acted "dishonestly and irresponsibly" in conducting his research that established a link between autism and the MMR vaccine. And yesterday, the British medical journal Lancet finally retracted the resulting 1998 study authored by Wakefield that helped drive MMR vaccination rates in the U.K. down to the point where in 2008, measles was officially declared "endemic" in the country.
The Lancet's editor, Richard Horton, told The Guardian "It was utterly clear, without any ambiguity at all, that the statements in the paper were utterly false," he said. "I feel I was deceived."
The GMC investigation, entailing 197 days of evidence, submission and deliberation between July 2007 and January 2010, exposed an unscrupulous researcher who falsified data, used sloppy laboratory techniques and subjected children to painful and potentially harmful medical tests like lumbar punctures and multiple colonoscopies to try and prove his notion that MMR vaccinations cause bowel disease and autism. Wakefield even went so far as to offer children attending his son's birthday party £5 to donate blood samples.
The investigation of Wakefield and his shoddy and unethical research methods began in 2004 when British journalist Brian Deer began talking with parents of the 12 children involved in Wakefield's study and reviewing medical records. Since then, Deer has dedicated countless hours and words to setting the record straight about Wakefield's work—including the finding that his research was funded by lawyers representing parents who planned to sue vaccine makers for damages.
Click here to see a collection of Deer's articles in the Sunday Times over the years that continued to drive this case even when Lancet editors refused to revisit the research. In a synopsis of the investigation Deer writes:
"Although in January 2010 a UK General Medical Council [GMC] panel concluded a mammoth, multi-part hearing with findings that Wakefield's conduct and research was both 'dishonest' and 'unethical', the published study's principal 'finding' was an alleged association between MMR vaccination and what the Wakefield group claimed to be the sudden onset of developmental disorders in eight – two-in-three – of the 12 children.
"This 'finding', and massive publicity that the Royal Free hospital and medical school encouraged for it launched a worldwide scare over the vaccine's safety, triggering falls in immunisation rates, outbreaks of potentially fatal or disabling diseases, and an epidemic of self-recrimination among parents of autistic children."
There is a lesson here that is especially pertinent in these media-saturated times. A study like Wakefield's—hyped by a credible medical institution, and reported uncritically by the 24-hour news outlets, the blogosphere, social media sites and Twitter—can quickly become accepted dogma with dire consequences to public health. In just a short amount of time, Wakefield's theories of how MMR causes damage to the gut and subsequently, autism spectrum disorder became the basis for anti-vaccine efforts both in Europe and the U.S. that continue unabated today.
Where is Andrew Wakefield now? He is currently Executive Director at the Thoughtful House, a center for autistic children in Texas. He and his fellow researchers at this center continue to plumb the connection between bowel disease and autism. Wakefield's supporters–most of whom are parents of autistic children– still stand by his work, accusing the GMC of "censorship" and citing evidence of a nefarious plan to silence those researchers who stand behind the vaccine-autism link. It's really depressing that these parents—perhaps grasping for straws–continue to support someone who not only is dishonest, but intentionally subjected small children to painful medical tests for no good reason.
Autism continues to be an enormous public health issue in this country and elsewhere around the world. In the U.S. it affects an estimated 1 in 100 births and is growing at a rate of 10-17% year. It is easy to understand why parents of affected children would welcome news about the cause and prevention of this debilitating disorder. But over the last decade we have seen scarce resources squandered on study after study disproving the connection between vaccines and autism and disproving questionable
treatments like extreme diets and chelation therapy.
In the end, the true story is that researchers are still at the basic stages of figuring out what really causes autism. It's likely that there will be several, if not many, causes of the disorder. Obama's new budget has
provided $222 million (an increase of $16 million) for studying Autism Spectrum Disorders (ASD). Much of this funding is going towards basic research: "innovative approaches to defining the genetic and
environmental factors that contribute to ASD, investigate epigenomic changes in the brain, and accelerate clinical trials of novel pharmacological and behavioral interventions."
Let's hope that ultimately, the Wakefield experience will have a positive influence on the autism field—leading to greater oversight, more rigorous research and a marginalization of the junk science that has dominated public debate for too long.
Tagged as: Autism, Immunizations, public health, Social Media
American Healthcare: Caught in a Bad Romance
The role of the lawyers in helping should not be ignored here.Their actions deserve to be called into question. Read the article…
http://marketsandculture.blogspot.com/2011/01/mmr-vaccine-autism-andrew-wakefield-and.html
..it is astonishing how many millions of public money were wasted by their actions.
MD as HELL says:
Evidence-based medicine at its worst and most feared. Can we also say "climate change"? There is no such thing as "settled science".
just because you can't prove one, doesn't mean it's not connected – high fevers from vaccines can result in brain damage, diagnosed in the autism spectrum…i don't need proof; my grown brother has been institutionalized most of his life after being a healthy 2-year old – no one's in favor of false research, but there is no doubt in any family of kids that have suffered this result.

redpajama_set_name: RedPajamaCommonCrawl


text:
[Thread Prev][Thread Next] >Date Index >Thread Index
What they say and what they mean
Hans Maurer - Thu Sep 03 20:51:20 1998
What they say and what they mean:
The support person's guide to understanding the user:
"I didn't do anything to it..."
A.K.A.   (I got an out of MEMORY error, so I cleaned up my hard drive a
little)
"My modem was working yesterday..."
A.K.A.   (the one at the office worked, I've never actually tried to use
this one)
"My CD-ROM drive is C:, yes I'm sure"
A.K.A. (I've never done anything with my computer except listen to music)
"It crashed! Everything is black!"
A.K.A. (I'm at the DOS prompt)
"Yes, I emptied the trash can, but I've ALWAYS been able to get it back"
A.K.A. (I found my trash can today, and practiced moving my icons in and
out of it)
"I AM pushing the right mouse button"
A.K.A. (The other button is the wrong one)
"Yes, I'm sure it's plugged in!
A.K.A. (The power strip is plugged into itself)
"I DO have a modem"
A.K.A. (The salesman said this computer would even cook my breakfast. I
believed him)
"What do you mean it doesn't work in DOS?"
A.K.A. (I don't want it to work in DOS, I just want it to work in Works
"This IS an emergency"
A.K.A. (I'm addicted to the net, my friends are expecting me!)
"I've got this little problem"
A.K.A. (This is only the beginning of a list of problems I've been saving
up - but I want you to like me.)
"It just started doing this"
A.K.A. (I never tried it before this)
"My mouse won't move"
A.K.A. (The hourglass is still churning)
"I did do the START button"
A.K.A. (I pushed the Power button)
"My modem IS plugged in"
A.K.A. (Into the PHONE jack)
"I understand"
A.K.A. (I comprehended two words in the last 10 minutes) Those words are
usually "Ok now.."
"I can't find the Start button..."
A.K.A. (Can't you just fix it over the phone without my help??)
What they say and what they mean, Hans Maurer
Ten Reasons..., Hans Maurer
Blonde Joke, Markus Wennrich
Fun: Internet Junkie, Markus Wennrich
Fun: Checking Meters, Markus Wennrich


text:
LAB #3 Working on Sex Education / Music Production and Composing
Part of: BFI Film Academy Labs Part of: Break into film: BFI Film Academy North
Thu 9 Dec 2021
Ever wondered how shows like the Netflix teen comedy-drama Sex Education create their original music? Or what exactly a composer working in film and TV does? Want to know how to create a music that elevates a film with or without a big budget?
Oli Julian, Sheffield based composer, producer and arranger of music for Netflix, TV, commercials and indie feature films will share his career journey, creative practice and collaborations and give us some insights into scoring music for comedic and dramatic moments in shows like Sex Education.
Oli's work was nominated in the Best TV Score category at the Ivor Novello Awards 2021 and he has worked on Independent Feature, How to Build a Girl, based on the novel by Caitlin Moran and a wide range of shows including Catastrophe (C4), Cuckoo (BBC3) and Plebs (ITV2), as well as Josh (BBC3), Cockroaches (ITV2), GapYear (C4), Crashing (C4), Year Of The Rabbit (C4).
This session will be of particular interest to anyone interested in film, soundtracks and music production.
This is a free online event.
This activity is part of the UK-wide BFI Film Academy programme and is supported using funds from the National Lottery #NationalLottery @bfifilmacademy. BFI Film Academy North is led in partnership by HOME, Manchester and Showroom Cinema, Sheffield working with a wide-ranging group of exhibitors and filmmakers throughout the region.
Credit: Photo by Elijah Merrell on Unsplash
Open to 16-25 year olds. This is a free online event.
Registration for this event is now closed, you can join the mailing list to be kept informed of forthcoming event below


When Chicago drivers need Lincoln repair, they turn to the auto mechanics at Zepeda Auto Repair. As the leading auto repair shop in the region, you can count on our technicians to properly service and repair your Lincoln, while checking in with you each step of the way.
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From hearing the unusual squeak stemming from your steering wheel to how much your beloved vehicle means to you, the team at Zepeda Auto Repair takes the time to listen to you and your car. We know the value of hard work, as we won't rest until your foreign or domestic vehicle is performing in pristine condition. Schedule an appointment today for Lincoln service. Zepeda Auto Repair is conveniently located at 3061 W. Armitage Ave., Chicago, IL 60647, and our hours of operation are Mon - Fri: 8:00 AM - 5:00 PM and Sat: 9:00 AM - 2:00 PM.


text:
Veteran director and actors deliver epic The Irishman to lucky audiences
At this point in his career, director Martin Scorsese can do almost anything he wants to do. The success he's had during his 60 years in Hollywood, which now includes 25 feature films, is second-to-none, and has earned him the right to deliver whatever material he so desires. In the case of his latest, The Irishman, that's a 209-minute epic about the mob, unions, and the death of Jimmy Hoffa.
The film centers on Frank Sheeran (Robert De Niro), who's working as a truck driver in the 1950s when he becomes connected with Russell Bufalino (Joe Pesci), the leader of the Bufalino crime family. Soon, Frank is doing all sorts of work for Russell, including killing the occasional person who steps out of line.
Their mutual interests lead them into the orbit of International Brotherhood of Teamsters Union President Jimmy Hoffa (Al Pacino), who has no trouble throwing his weight around to get his way. For years, Sheeran essentially plays both sides, acting as enforcer for Bufalino and working his way up the ranks with the Teamsters for Hoffa.
Scorsese, working from a script by Steve Zaillian, is more than happy to get down into the weeds of both the crime business and union politics. There is no point A to point B in this movie, as the timeline switches so often that it's nearly impossible to keep up. Scenes take place over the course of 50 years, with both De Niro and Pesci sporting multiple different looks to demonstrate aging and de-aging (Pacino is also de-aged to a lesser degree).
It's here where the much-vaunted de-aging technology crops up. And, for the most part, the computer-generated effects work seamlessly. Other than the initial curiosity of seeing the two 76-year-old actors look years younger, the nature of the story takes away any intense scrutiny of whether their faces look exactly right. In fact, other than the shockingly blue eyes De Niro sports throughout, it's when De Niro and Pesci are shown to be their age or older when their faces become the most interesting.
Scorsese has long loved using voiceovers to further the plot of his films, and the technique is in full effect in The Irishman. In an odd way, the voiceover by De Niro is highly reminiscent of Ray Liotta's voiceover in Goodfellas, as his character — like Liotta's Henry Hill — moves from lowly underling to the top of the food chain. The narration, unlike in some other films, enhances the understanding of the film instead of feeling like a superfluous addition.
The movie hinges on the performances of De Niro, Pesci, and Pacino, and each is as good as he has ever been. They're so famous that they can sometimes be viewed as caricatures of themselves, but this film is a reminder that all three are among the best actors working today. A bevy of great supporting actors like Bobby Cannavale, Ray Romano, Anna Paquin, Harvey Keitel, Jesse Plemons, and more keep the film humming throughout.
It is fortunate the film has a wealth of acting talent as the extended running time definitely takes its toll. After running for a few weeks in theaters, most viewers are likely to watch the film at home on Netflix, and the ability to take a break every now and again will likely enhance the pleasure of watching the film. The plot goes down so many similar storylines that, in the theater, it feels like Scorsese could have cut an hour off of the film and not sacrificed the story.
Scorsese has been making movies so long that he's been counted out multiple times before roaring back and showing how capable he truly is. While perhaps a step below his other classic films, mostly because of its length, The Irishman shows that the master filmmaker still knows how to deliver a crackerjack story.
The Irishman is currently playing at Landmark Magnolia, iPic Fairview, and Studio Movie Grill Spring Valley. It will debut on Netflix on November 27.
Robert De Niro and Joe Pesci in The Irishman.
Photo courtesy of Netflix
Nickelback's upcoming tour stop rocks this week's 5 hottest Dallas headlines
Nickelback/Facebook [https://www.facebook.com/Nickelback]
1. Juggernaut rock band Nickelback is touring summer 2023 with stop in Dallas. Nickelback is back: Canadian-born rock juggernaut Nickelback is going on tour in summer 2023 to support their new album, Get Rollin'. Called the "Get Rollin' Tour," it'll hit 38 cities, including Dallas on July 22 at Dos Equis Pavilion.
2. Behind the wall of greenery and other Dallas restaurant must-haves. If you're a Dallas restaurant in 2023, you're nowhere without a wall of greenery. Walls covered with greenery are among the features restaurants are deploying these days to lure in diners. Food is still the official reason people go out to eat, but restaurants these days are more experience-oriented. Here are few features being rolled out at restaurants around town.
3. Affluent Dallas neighbor cashes in as the richest city in Texas for 2023. North Texans wanting a glimpse into the lives of the 1 percent won't have to travel far to get a peek. Southlake has been named the richest city in Texas for 2023 in a recent study.
4. South Polk Pizzeria in Dallas' Oak Cliff slings perfect pies in pizza desert. Dallas has plenty of pretty pizza these days — but nearly all of it is found north of I-30. So let's hear it for South Polk Pizzeria, a new shop that opened in late December in Oak Cliff, at 3939 S. Polk St #527, just off US-67 and north of Loop 12, slinging the same kind of artisanal pies that are being slung across Deep Ellum, Oak Lawn, and North Dallas.
5. 12 Dallas-Fort Worth restaurants score coveted James Beard Award nominations. The James Beard Foundation has revealed the semifinalists for its 2023 Restaurant and Chef Awards. A dozen Dallas-Fort Worth restaurants have been included in both national and regional categories. Here are the nominees.
concertspizzahot-headlines


Read by Dennis Quaid ; with January LaVoy, Peter Ganim, Jeremy Davidson, and Mozhan Marnò.
by Bill Clinton and James Patterson.
Add tags for "The president is missing : a novel". Be the first.
FICTION / Thrillers / Political .
schema:about <http://experiment.worldcat.org/entity/work/data/4760918865#Place/united_states> ; # United States.


text:
Perspective //
Eliza Bicego on the ethics of Coke.
by Eliza Bicego
James is a vegan.
He has so far managed to dodge the siren call of 4am doner kebabs and, has maintained the right to hold his head high and ask with conviction, "I'm sorry, but is there dairy in the vegetarian flan?" He questions a barista for twenty minutes on the Fair Trade certification of their Arabica, and has heated conversations about unsustainability and inherent waste in the production of beef.
James also does coke.
Which is awkward, because coke is pretty shit for the environment.
Growing coke leads to massive deforestation in rural Colombia. About five hundred square kilometres are cleared each year for the growth and production of narcotic substances. The Colombian Andes have suffered for the world's little habits; experts say that around 73 per cent of the local ecosystem has been negatively affected by the growth, production and transportation of cocaine.
People, like James, are big enough and ugly enough to make their own decisions— it isn't just cocaine that's fucking up South America. The Maggi Group is also partially responsible for the severe deforestation of the Amazon rainforest in Brazil, but I'm not going to give up noodles anytime soon.
What strange blind spots we have in our collective consciousness. It can make hypocrites of us all.
narcontics
The "Battle of the Wasser": A Footnote in History?
By Robbie Mason
The racist and normative life of Sims 4
By Karishma Luthria
The roaches of Fisher are moving upstairs
By Anh Nguyen
Wentworth Must Fall
By Himath Siriniwasa and Georgia Mantle
From the mines
SRC Reports
We acknowledge the traditional custodians of this land, the Gadigal people of the Eora Nation. The University of Sydney – where we write, publish and distribute Honi Soit – is on the sovereign land of these people. As students and journalists, we recognise our complicity in the ongoing colonisation of Indigenous land. In recognition of our privilege, we vow to not only include, but to prioritise and centre the experiences of Indigenous people, and to be reflective when we fail to be a counterpoint to the racism that plagues the mainstream media.
Copyright Honi Soit 2018.


text:
El Museo Nacional y Galería de Arte de Papúa Nueva Guinea (NMAG) es un museo y galería de arte ubicada en Waigani, Puerto Moresby, Papúa Nueva Guinea. Es el museo nacional de Papúa Nueva Guinea.

La colección del museo se centra en tres áreas principales, el arte tradicional de Papúa, la producción contemporánea y los objetos relacionados con la Campaña de Nueva Guinea durante la Guerra del Pacífico.

Historia
La junta de la administración para el establecimiento del museo propuesto fue formado en 1954. La construcción del museo comenzó en 1975 y su inauguración pública fue en 1977.

Galerías
El museo consiste en la Galería Masterpiece, la Galería Sir Michael Somare y la Galería Independence.

Referencias

Enlaces externos

 Sitio web oficial

Museos inaugurados en 1977
Edificios y estructuras de Puerto Moresby
Museos nacionales

redpajama_set_name: RedPajamaWikipedia


text:
U.S. movie adaptation of 'Death Note' inches forward with new director
Black conservative activist made #IceBae a thing
Ramon Ramirez— July 17
As the Border Patrol reveals controversial #ICEBae's true identity and says she's free to post more selfies, it turns out her viral fame was a targeted troll.
70 Border Patrol employees under investigation for posts in secret Facebook group 4 Years Ago
Republican's Operation Safe Return criticized as cover for mass deporation 4 Years Ago
AirTV is essential for Sling TV subscribers Today 6:00 AM
Aja Romano—
Illustration by xxpoizonedmuffinzxx/deviantART
Like Light Yagami himself, this beleaguered production just won't quit.
Just when it seemed it was finally dead in the water, the English-language production of Death Note has found a way to live on.
Adam Wingard, who helmed such cult horror films as The Guest and You're Next (the latter is high on our list of best horror films on Netflix) has signed onto direct the English movie adaptation of the bestselling manga and anime series, according to the Hollywood Reporter.
Wingard steps into the director's chair after the departure of Shane Black (Kiss Kiss, Bang Bang). He joins a production that has been in the works for years, after the film rights bounced around Hollywood, transferring to Warner Bros. in 2009. At this point, the English-language Death Note has had so many unbelievable rumors attached to it that it's taken on something of a mythic quality. No, Zac Efron is not starring as Light Yagami, the model teen whose angelic face masks a megalomaniacal sociopathy that finds its perfect companion when he chances upon a "death note"—a book that kills whoever's name is written in it, in the manner of the author's choosing. No, Gus van Sant is not coming on board as director. No, the leaked script that surfaced in 2011 is not the one currently in production, and the demon Ryuk, who gives Light the Death Note, will not be written out of the final version.
The newest script by Jeremy Slater (Fantastic Four) will restore many of the elements in the source material. With indie horror veteran Wingard at the helm, and with a production team that now includes die-hard anime fan Masi Oka of Heroes fame, things may actually be looking up for this dark, modern morality tale.
Still, this is only the beginning of a long road to movie theaters. In particular, the ultimate question has yet to be answered: Who will be worthy of playing Light and his iconic nemesis, the socially awkward, reclusive genius L? Since so much of the manga and the anime's popularity rests on the shoulders of these two characters, the casting choices here could make or break the production before filming even starts—hence the persistent rumors about Efron from a horror-stricken fanbase.
By now, 12 years after the Death Note manga first riveted Japan, so much drama currently exists around its English-language adaptation that we worry there's no way for the film to live up to the hype.
But still, even a Death Note adaptation that's all flash, like Light himself, would be better than no film at all.
And with this news, we're one step closer to finally seeing it for ourselves.
Illustration by xxpoisonedmuffinzxx/deviantART (CC BY 3.0)
Aja Romano is a geek culture reporter and fandom expert. Their reporting at the Daily Dot covered everything from Harry Potter and anime to Tumblr and Gamergate. Romano joined Vox as a staff reporter in 2016.
Anime Death Note Film Japan Manga Warner Bros Zac Efron


text:
Coos Bay and Coquille Transportation Co. records
Collection — Multiple collection box: [B 111] 1
Identifier: B 111
University of Oregon Libraries, Special Collections and University Archives
The collection (July 11, 1873 - February 11, 1876) contains meeting minutes, and treasurer's reports.
Minutes include meetings of the board of directors and also the stockholders.
1873, July 11 -1876, February 11
Coos Bay and Coquille Transportation Company (Organization)
Collection is open to the public. Collection must be used in Special Collections and University Archives Reading Room. Collection or parts of collection may be stored offsite. Please contact Special Collections and University Archives in advance of your visit to allow for transportation time.
Property rights reside with Special Collections and University Archives, University of Oregon Libraries. Copyright resides with the creators of the documents or their heirs. All requests for permission to publish collection materials must be submitted to Special Collections and University Archives. The reader must also obtain permission of the copyright holder.
0.025 linear feet (1 container) : 1 volume
Coos Bay and Coquille Transportation Co. was a steamboat company and the original directors were Nat H. Lane, Charles Merchant, and C. B. Golden. The collection (July 11, 1873 - February 11, 1876) contains meeting minutes and treasurer's reports.
Donated by Mr. and Mrs. Edward Shepherd, in 1942.
Collection processed by staff.
This finding aid may be updated periodically to account for new acquisitions to the collection and/or revisions in arrangement and description.
This collection received a basic level of processing including minimal to no organization and rehousing.
Description information is drawn in part from information supplied with the collection and initial surveys of the contents.
Businesses and Corporations Subject Source: Archiveswest
Coos Bay (Or.) -- History -- 19th century Subject Source: Lcnaf
Coos Bay and Coquille Transportation Company
Coquille (Or.) -- History -- 19th century Subject Source: Lcnaf
Financial records Subject Source: Art & Architecture Thesaurus
Oregon Subject Source: Archiveswest
Steamboat lines -- Oregon Subject Source: Library of Congress Subject Headings
Transportation -- Oregon Subject Source: Library of Congress Subject Headings
LOC/V. Naval Science
LOC/H. Social Sciences
LOC/H/H11. Transportation and Communication
Guide to the Coos Bay and Coquille Transportation Co. Records
Complete Description
Tanya Parlet and Kira B. Homo.
Finding aid written in English.
Funding for production of this finding aid was provided through a grant awarded by the National Historical Publications and Records Commission (NHPRC).
Multiple collection box: [B 111] 1 (Mixed Materials)
Part of the University of Oregon Libraries, Special Collections and University Archives Repository
http://libweb.uoregon.edu/speccoll
Eugene OR 97403-1299 USA
[Identification of item], Coos Bay and Coquille Transportation Co. records, B 111, Special Collections & University Archives, University of Oregon Libraries, Eugene, Oregon.
[Identification of item], Coos Bay and Coquille Transportation Co. records, B 111, Special Collections & University Archives, University of Oregon Libraries, Eugene, Oregon. https://scua.uoregon.edu/repositories/2/resources/1204 Accessed January 22, 2022.
Collection information
Resources previously consulted
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yes no unknown


I had a busy weekend, so, on Monday, I didn't check emails from Medium until late afternoon. My Medium notifications during the weekend were quite inexistent, so there was no activity at all to handle.
Just a few days before I was complaining about the 7 views on a story. Then, on Friday, I published an article which had some feedback mainly by the mentioned people (around 30 views), but that's all. A silent weekend followed.
Only 156 followers and deafening silence.
When I finally checked emails from Medium, I had a surprise. Top Writer in Social Media. The email was of Sunday and I didn't even notice, among the daily digests.
Going briefly back to my first Top Writer (in Writing), 45 days before, I had a similar silent condition, but then one story suddenly got feedback, and after that I got the Top Writer award.
A burst of four days of comments, clapping and new followers. Around 2k views. A completely different situation. I expected an award only in a similar scenario.
Well, I was glad of this additional award, of course, but there must have been a mistake. Where the hell did it jump out?
1k? A story with only 8 fans and no comments?
Well, that story, of many days before, had around 400 views, and more than 500 additional external views. I didn't think that these external views really count, but they do.
So, a story with around 400 views non-RSS, 8 fans (the 9 you see is nowadays) and no comments. A very poor scenario, for an award… I like that story, but definitely it's not a masterpiece, and the feedback has been literally inexistent.
And slightly more than 1k seems much less than the views that triggered my previous award. Maybe something else contributed, as we will see.
After only three days, amid a persistent terrific silence, the third award (in Reading).
Better not to ask Medium, but I was convinced that something was really wrong with their algorithm.
Then, I started to think about a behavior I adopted weeks ago.
In one of his articles, kevin wrytes opened my eyes on tagging. I was aware of them before, but since that article I took the habit of keeping a spreadsheet of my favourite tags. Included my target tags. I turned my attention to tags into a tactical use of them.
In that spreadsheet I note if they are target tags for me (topics about which I think I have something to say), if they are handpicked by Medium, if there is the Top Writer list for them and some other few data that, to be honest, I don't care too much yet but I've started tracking for the future.
I still don't have a well-defined strategy in blogging. I leave my inspiration quite free. But there are topics I tend to explore more, so I did two things.
The first is to pay attention to include one (at least) of my target tags in my stories (when relevant, of course). And I choose my target tags among those with a Top Writers list (the possibility of handpicking is a good plus).
The second is usually to add tags to my comments. And as they are generally about topics I'm interested in (of course!), I take care of letting my target tags slip in when they are relevant. I don't comment lightly. I often read/clap without commenting and I'm not interested in changing this attitude. But when I comment, I often include tags.
But how did that exactly lead to my two awards?
In November, in eight days, I already wrote 47 comments.
In October my comments were more than one hundred.
Many of them include my target tags. Many of them have dozens of views, if not hundredths of views. I would be happy if my stories had the same stats of my comments.
This easily added over a thousand of views per month to each of my target tags. That, added to the recurring tags in the stories, did the trick.
I would say that my Top Writer in reading is someway deserved. Maybe not because I write so much about reading but because I, um… read a lot!
Even if I adopted the tactic tagging, I was not focusing my stories on certain topics. It happened. I just paid attention to include one among my target tags when relevant. My lesson was that focusing pays and I should also pay attention in picking among my tons of ideas in a more focused way (but not too much!).
The third lesson is that I gained only three new followers in a whole week. The Top Writer awards don't help stats. For that, you need consistency and exposure, not awards.
Finally, I learned that the advice on social media from some people who succeeded before you really work. Kevin, these two additional awards arrived thanks to your advice. Thank you!


text:
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i-SIGMA Expands Government Relations Monitoring Capabilities
As of next month, i-SIGMA will begin monitoring proposed legislation with new state-of-the-art software designed specifically for that purpose.
"We really had no choice," said i-SIGMA CEO Bob Johnson, "There are simply too many regulatory developments to track manually, and stakes are too high to leave it to chance."
The new software will allow the i-SIGMA Government Relations Committee to track every proposed bill in the U.S., at both the state and federal levels, as well as provide updates, and prompt i-SIGMA when there's an opportunity to provide comments or participate in hearings.
"The Board is excited about this new tool, as it will empower every member to get involved and directly sync them up with their elected officials," said Angie Singer Keating, co-President of i-SIGMA. "They can share important legislation with their customers as well, spreading grassroots advocacy. All of this has the potential to have a massive impact on upcoming data protection laws."
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Roast chicken is pretty much perfect. Great flavor. Succulent. Absolutely satisfying.
Get your oven HOT, 475. Rinse the chicken and pat the skin dry. Throw a lemon half in the cavity. Season liberally with salt & pepper. Tie the legs together and tuck the wings underneath. And roast for 50-55 minutes (3-5 lb bird), until the internal temperature is 165 and the juices run clear. Let rest for 10-15 minutes. Carve and serve. I don't carve very well. I usually only carve the breast & legs. The rest we save for picking at later.
Sometimes you're given an excuse to eat the skin.
This? Is one of those times.


Effects of substance abuse affect every part of a person's body, mind, and life. Using drugs can result in a wide range of physical effects. Cocaine for example can produce effects such as depression, anxiety, and fatigue. Marijuana and alcohol affect motor control and reasoning and can result in automobile accidents or other physical accidents. Many marijuana users also experience flashbacks even weeks or months after use. Using heroin can result in nausea, vomiting, convulsions, delirium and muscle cramps. With many drugs, the effects of substance abuse are apparent when needles are shared resulting in HIV/AIDS or hepatitis.
Do You Know the Effects of Substance Abuse?
The effects of substance abuse are also evident in the workplace and at home. Drug cravings affect the person's mood and performance, and can have negative effects on all areas of the individual's life. Pregnant women have a higher rate of low low birth-weight infants. Additionally, risks to a fetus occur when the drug crosses the placenta and causes the infant to go through withdrawals soon after birth.
Substance abuse effects in the workplace can become costly. This happens when the individual misses work or is inefficient in performing their tasks. These individuals are also more prone to accidents that put others in danger of physical harm that can cost the company money.
The effects of substance abuse and addiction on society are staggering. In the US alone, billions of dollars are spent yearly on health care, treatment, legal fees, and law enforcement due to the drug-related crime and illnesses.
Other effects of substance abuse are evident when neighborhoods are disrupted with violence such as threats to residents from drug dealers or crimes committed by the addicts themselves. The biggest danger to society occurs in neighborhoods where dealers recruit young kids to be helpers or lookouts, bringing guns into an otherwise quite, safe area.
The worst effects of substance abuse is for the addict to become homeless or die all alone somewhere. Many city alleyways are filled with addicted people who are suffering needlessly from drugs.
If you would like more information about the effects of substance abuse, call today and we will be happy to answer any questions you might have.